Kalman PSaR [BackQuant]Kalman PSaR
Overview and Innovation
The Kalman PSaR combines the well-known Parabolic SAR (PSaR) with the advanced smoothing capabilities of the Kalman Filter . This innovative tool aims to enhance the traditional PSaR by integrating Kalman filtering, which reduces noise and improves trend detection. The Kalman PSaR adapts dynamically to price movements, making it a highly effective indicator for spotting trend shifts while minimizing the impact of false signals caused by market volatility.
Please Find the Basic Kalman Here:
Kalman Filter Dynamics
The Kalman Filter is a powerful algorithm for estimating the true value of a system amidst noisy data. In the Kalman PSaR, this filter is applied to the high, low, and closing prices, resulting in a smoother and more accurate representation of price action. The filter’s parameters—process noise and measurement noise—are customizable, allowing traders to fine-tune the sensitivity of the indicator to market conditions. By reducing the impact of noise, the Kalman-filtered PSaR offers clearer signals for identifying trend reversals and continuations.
Enhanced PSaR Calculation
The traditional Parabolic SAR is a popular trend-following indicator that highlights potential entry and exit points based on price acceleration. In the Kalman PSaR, this calculation is enhanced by the Kalman-filtered prices, providing a smoother and more reliable signal. The indicator continuously updates based on the acceleration factor and max step values, while the Kalman filter ensures that sudden price spikes or market noise do not trigger false signals.
Min Step and Max Step: These settings control the sensitivity of the PSaR. The Min Step sets the initial acceleration factor, while the Max Step limits how fast the PSaR adapts to price changes, helping traders fine-tune the indicator’s responsiveness.
Optional Smoothing Techniques To further enhance the signal clarity, the Kalman PSaR includes an optional smoothing feature. Traders can choose from various smoothing methods, such as SMA, Hull, EMA, WMA, TEMA, and more, to reduce short-term fluctuations and emphasize the underlying trend. The smoothing period is customizable, allowing traders to adjust the indicator’s behavior according to their preferred trading style and timeframe.
Color-Coded Candle Painting The Kalman PSaR features color-coded candles that change according to the trend direction. When the price is above the PSaR, candles are painted green to indicate a long trend, and when the price is below the PSaR, candles are painted red to signal a short trend. This visual representation makes it easy to interpret market sentiment at a glance, improving decision-making speed during fast-moving markets.
Key Features and Customization
Kalman Filter Customization: The process noise and measurement noise parameters allow traders to adjust how aggressively the filter adapts to price changes, making it suitable for both volatile and stable markets.
Smoothing Options: A variety of moving average types, such as SMA, Hull, EMA, and more, can be applied to smooth the PSaR values, ensuring that the signal remains clear even in choppy markets.
Dynamic Trend Detection: The Kalman PSaR dynamically updates based on price movements, helping traders spot trend reversals early while filtering out false signals caused by short-term volatility.
Bar Coloring and PSaR Plotting: Traders can choose to color candles based on trend direction or plot the PSaR directly on the chart for additional visual clarity.
Practical Applications
Trend-Following Strategies: The Kalman PSaR excels in trend-following strategies by providing timely signals of trend changes. The dynamic nature of the indicator allows traders to capture significant price movements while avoiding market noise.
Reversal Identification: The indicator’s ability to filter out noise and provide smoother signals makes it ideal for identifying reversals in volatile markets.
Risk Management: By plotting clear stop levels based on the PSaR, traders can use this indicator to effectively manage risk, placing stop-loss orders at key points based on the trend direction.
Conclusion
The Kalman PSaR is a fusion of the classic Parabolic SAR and the Kalman filter, offering enhanced trend detection with reduced noise. Its customizable filtering and smoothing options, combined with dynamic trend-following capabilities, make it a versatile tool for traders seeking to improve their timing and signal accuracy. The adaptive nature of the Kalman filter, combined with the robust PSaR logic, helps traders stay on the right side of the market and manage risk more effectively.
Statistics
TradeTracker v33 - Interactive Journal [AR33_]TradeTracker v33 - Interactive Journal is a unique tool designed to enhance your trading experience by integrating an interactive journal directly onto your charts. Unlike traditional trading journals that require manual entries outside of TradingView, this script allows traders to document, track, and review their trades in real-time, right where the action happens.
What sets TradeTracker v33 apart from existing tools is its seamless blend of note-taking, task management, and performance tracking—all within a single, intuitive interface. With features like customizable checklists, due dates, and color-coded status indicators, this script provides a powerful and practical solution for traders who want to stay organized and disciplined.
2. Description
. TradeTracker v33 - Interactive Journal is designed to keep traders on track by allowing them to record trade-related notes, set tasks, and mark progress directly on their charts.
Here’s how it works:
• Purpose: The script serves as an all-in-one journal and task manager, helping traders document their trading strategies, track ongoing tasks, and review completed actions. It’s particularly useful for maintaining discipline and ensuring that every trade is executed according to a well-thought-out plan.
• How It Works:
• Interactive Notes and Tasks: Users can create and manage notes and tasks directly on their charts. Each note can be customized with a title, description, due date, and completion status.
• Status Indicators: Tasks are color-coded based on their status—green for completed, red for overdue, and default colors for pending tasks—allowing traders to quickly assess their progress.
• Dynamic Display: Notes are displayed in a clean, organized table on the chart, making it easy to review multiple tasks without cluttering the trading interface.
• Usage:
• Adding Notes: Simply fill in the note title, content, and optional due date within the script’s input settings, and the note will appear on your chart.
• Tracking Progress: Mark tasks as completed with a simple toggle, and the script will update their status in real-time.
• Customizing Your Workflow: Adjust the position, size, and visibility of notes to fit your trading style, ensuring that your journal supports rather than distracts from your trading activities.
3. Chart Presentation
To provide a clear and focused user experience, TradeTracker v33 - Interactive Journal is designed to be the sole feature on your chart when published. This ensures that users can easily identify and interact with their notes and tasks without any unnecessary distractions.
• Clean and Focused Display: The chart will exclusively display the interactive journal, showcasing how tasks and notes appear and update in real-time as you manage them.
• Useful Annotations: Annotations such as checkboxes and status indicators are clearly explained within the script’s description and are vital to understanding the functionality of the tool.
• Minimal Distractions: Only elements directly related to the script’s functionality are included on the chart, ensuring that users can easily follow along and implement the script in their own trading setup.
Enhanced Local Polynomial Regression [Yosiet]Local Polynomial Regression (LPR) is an advanced statistical method that offers a flexible approach to estimating the underlying trend in financial time series data.
The Mathematical Explanation
The core idea of LPR is to fit a polynomial of degree p at each point x using weighted least squares. The weight of each data point decreases with its distance from x, controlled by a kernel function and a bandwidth parameter.
The general form of the local polynomial estimator is:
β̂(x) = argmin Σ K((Xi - x) / h) (Yi - β0 - β1(Xi - x) - ... - βp(Xi - x)^p)^2
Where:
β̂(x) is the vector of estimated coefficients
K is the kernel function
h is the bandwidth
Xi and Yi are the predictor and response variables
p is the degree of the polynomial
Our implementation uses the Epanechnikov kernel:
K(u) = 3/4 * (1 - u^2) for |u| ≤ 1, 0 otherwise
The Implementation
This script implements LPR for the easier way to interpret its values with the following key components:
Input Parameters: Can adjust the lookback period, bandwidth, and polynomial degree.
Kernel Function: The Epanechnikov kernel is used for weighting.
LPR Function: Implements the core algorithm using matrix operations.
Signal Generation: Generates buy/sell signals based on crossovers of smoothed price and LPR results.
How to Use
Apply the indicator to your chart in TradingView.
Adjust the input parameters:
Lookback Period: Controls how many past bars are considered.
Bandwidth: Affects the smoothness of the regression line.
Polynomial Degree: Determines the complexity of the local fit.
Signal Smoothing Length: Adjusts the responsiveness of buy/sell signals.
Monitor buy/sell signals for potential trade entries.
Limitations
Sensitivity to Parameters: The choice of bandwidth and polynomial degree significantly impacts the results.
Lag: Like all trend-following indicators, LPR may lag behind rapid price movements.
Edge Effects: The indicator may be less reliable at the edges of the data (recent bars).
Recommendations
Parameter Optimization: Experiment with different lookback periods, bandwidths, and polynomial degrees to find the best fit for your trading style and timeframe.
Combine with Other Indicators: Use LPR in conjunction with momentum oscillators or volume indicators for confirmation.
Multiple Timeframes: Apply LPR on different timeframes to gain a more comprehensive view of the trend.
Avoid Overfitting: Be cautious of using high polynomial degrees, as they may lead to overfitting on historical data.
Consider Market Conditions: LPR works best in trending markets; be aware of its limitations in ranging or highly volatile conditions.
Backtest Thoroughly: Always backtest strategies based on LPR across different market conditions before live trading.
Conclusion
Local Polynomial Regression offers a sophisticated approach to trend analysis in financial markets. By providing a flexible, adaptive trend line, it can help traders identify potential entry and exit points with greater precision than traditional moving averages. However, like all technical indicators, it should be used as part of a comprehensive trading strategy that includes proper risk management and consideration of fundamental factors.
if you have an strategy or idea and need to make it real through an indicator or trading bot, you can DM or comment
Honey Badger and Dip and Rip Days**Definitions**
A Honey Badger Day is defined as a day where the market dips below a certain threshold but then closes above it. Specifically:
- The day's low is less than or equal to the lower of either the opening price or the previous day's closing price.
- The day's closing price is greater than or equal to this same lower threshold.
Dip and Rip Day:A Dip and Rip Day is characterized by a more pronounced dip followed by a stronger recovery. The criteria are:
- The day's low is below 0.11% of the lower of the day's opening price or the previous day's closing price.
- The day's closing price is at least 0.405% higher than its opening price.
- The day's closing price is at least 0.792% higher than its low.
- The day's closing price is at least 0.405% higher than the previous day's closing price.
Both patterns indicate a day where the market experiences a dip but then recovers, with the Dip and Rip pattern showing a more dramatic movement in both directions.
Buy script for stocks mathematical calculation chart. it is totally based on the square root calculation of previous day + 66.66% of Square root. ( last dat sqrt+66.66% of Sqrt). buy above the value. best for stock in intraday
VWAP SlopePublishing one of the simplest yet one of my favorite concepts. Had to publish since I didn't really find any script for this on TV.
VWAP slope.
This is nothing fancy because it's just calculating "slope" with a very basic level formula
vwap_slope = (vwap - vwap ) / length
Above zero line, it's positive zone.
Below zero line, it's a negative zone.
The idea is to avoid choppy conditions and stay true to larger readings, sometimes when we have vwap directly on chart and when price interacts with it, we tend to take the lot of bad trades.
The intention here is to avoid just that.
This is also good at tracking failure of change in sentiments, this failure is very important, because one's failure occurs there is significant movement in the opposite direction of the failure.
Since there isn't much alteration to this idea, there is not much to talk about tbh.
Just remember, this is an educational idea and not assurance of future performance.
Regards.
Landry Light with Moving AverageLandry Light with Moving Average
Overview:
This Pine Script, titled "Landry Light with Moving Average", visualizes the relationship between price action and a chosen moving average (MA) over time. It helps users easily identify periods where the price stays consistently above or below the moving average, which can be a useful indicator of bullish or bearish trends.
Key Features:
Moving Average Type Selection:
The script allows users to choose between two types of moving averages:
Exponential Moving Average (EMA)
Simple Moving Average (SMA)
This is done via a user input option, enabling traders to tailor the indicator to their preferred analysis method.
Moving Average Length:
Users can set the length of the moving average (default is 21 periods). This allows customization based on the trader's time frame, whether short-term or long-term analysis.
Dynamic Moving Average Color:
The moving average line changes color based on the relationship between the price and the MA:
Green: Price is consistently above the MA (bullish condition).
Red: Price is consistently below the MA (bearish condition).
Blue: Price is crossing or close to the MA (neutral or indecisive condition).
Cumulative Days Above/Below MA:
The script tracks and displays the number of consecutive days the price remains above or below the moving average:
Cumulative Days Above: Shown as a green histogram above the zero line.
Cumulative Days Below: Shown as a red histogram below the zero line.
This feature helps users identify sustained trends or potential reversals.
Real-time Labels:
The script generates dynamic labels that display the count of cumulative days the price has stayed above or below the moving average.
These labels are positioned near the moving average on the chart, providing an easy reference for traders.
How Users Can Benefit:
Trend Identification:
By visually representing how long the price stays above or below a key moving average, traders can identify strong bullish or bearish trends. This can inform entry and exit points.
Visualizing Market Sentiment:
The colored moving average line and histogram help traders quickly assess market sentiment. A prolonged green MA line suggests a strong uptrend, while a prolonged red line indicates a downtrend.
Adaptability:
With customizable moving average types and lengths, the indicator can be tailored to fit various trading strategies, whether for day trading, swing trading, or long-term investing.
Reversal Signals:
A shift from cumulative days above to cumulative days below (or vice versa) can serve as an early signal of a potential market reversal, allowing traders to adjust their positions accordingly.
Simplified Decision-Making:
The combination of visual cues (colors, histograms, and labels) simplifies decision-making, allowing traders to focus on trend strength rather than complex calculations.
Usage:
To use this script:
Add the Indicator to Your Chart:
Select the desired moving average type and length.
The script will plot the moving average, colored by the trend, and display cumulative days above or below it.
Interpret the Signals:
Use the histogram and labels to gauge the strength of the trend.
Monitor color changes in the moving average for potential trend reversals.
Incorporate into Your Strategy:
Combine this indicator with other tools (e.g., volume analysis, RSI) to confirm signals and refine your trading strategy.
This indicator is particularly useful for traders who follow the "Landry Light" concept, emphasizing the importance of price staying above or below a moving average to determine trend strength.
DataDoodles ATR RangeThe "DataDoodles ATR Range" indicator provides a comprehensive visual representation of the Average True Range (ATR) levels based on the previous bar's close price . It includes both the raw ATR and an Exponential Moving Average (EMA) of the ATR to offer a smoother view of the range volatility. This indicator is ideal for traders who want to quickly assess potential price movements relative to recent volatility.
Key Features:
ATR Levels Above and Below Close: The indicator calculates and displays three levels of ATR-based ranges above and below the previous close price. These levels are visualized on the chart using distinct colors:
- 1ATR Above/Below
- 2ATR Above/Below
- 3ATR Above/Below
EMA of ATR
Includes the EMA of ATR to provide a smoother trend of the ATR values, helping traders identify long-term volatility trends.
Color-Coded Ranges: The plotted ranges are color-coded for easy identification, with warm gradient tones applied to the corresponding data table for quick reference.
Customizable Table: A data table is displayed at the bottom right corner of the chart, providing real-time values for ATR, EMA ATR, and the various ATR ranges.
Usage
This indicator is useful for traders who rely on volatility analysis to set stop losses, take profit levels, or simply understand the current market conditions. By visualizing ATR ranges directly on the chart, traders can better anticipate potential price movements and adjust their strategies accordingly.
Customization
ATR Length: The default ATR length is set to 14 but can be customized to fit your trading strategy.
Table Positioning: The data table is placed in the bottom right corner by default but can be moved as needed.
How to Use
Add the "DataDoodles ATR Range" indicator to your chart.
Observe the plotted lines for potential support and resistance levels based on recent volatility.
Use the data table for quick reference to ATR values and range levels.
Disclaimer: This indicator is a tool for analysis and should be used in conjunction with other indicators and analysis methods. Always practice proper risk management and consider market conditions before making trading decisions.
Z-Score AggregatorOverview:
This indicator is designed to take multiple other indicators as inputs, calculate their respective Z-scores, and then aggregate these Z-scores to provide a comprehensive measure. By transforming the inputs into Z-scores, this indicator standardizes the data, enabling a more accurate comparison across different indicators, each of which may have different scales and distributions.
This indicator is beneficial for Mean-Reversion style trading and investing as it standardizes indicators and lets them work together in one system.
The Z-score, which represents how many standard deviations an element is from the mean, is a crucial statistical tool in this process. It allows the indicator to normalize the varying data points, ensuring that each indicator's contribution to the aggregate score is proportional to its deviation from the average performance.
Inputs:
Z-score length: How far Back it will take into account the inputs
Number Of Sources: This is to set the number of inputs the indicator uses so it calculates them properly and uses only the number of indicators you want.
Source Inputs: 1-10 inputs (no need to use them all as long as you set the number of used indicators beforehand).
Note:
There are three indicators used in this example which are CCI, RSI and Sharpe Ratio. The indicator calculates their individual Z-scores and takes an average. Because Number Of Sources is set to 3 it only uses the first 3 indicators in use.
Cash Cycle BandCash cycle band shows the number of days and the profit margin compared to the previous period (it does not indicate how profitable the company is, but how well it is managed).
Cash cycle band consists of 6 sections:
1. DPO is the days payables outstanding in the "red" followed by O/D which is overdraft or short-term debt (if any) .
2. DIO is the days inventory outstanding in the "green" followed by classified inventory (if any) consists of finished goods. work in process and raw materials.
3. DSO is days sales outstanding in "blue".
4. DWC is days converting working capital to revenue in "orange".
5. CCC is days converting inventory and resources to cash flow in "yellow".
6. GPM is gross profit margin and OPM is operating profit margin.
The "😱" emoji indicates a value if it increases by more than or decreases by less than 20%, e.g.
- DPO, finished goods, work in process, raw materials, GPM, OPM is decreasing.
- O/D, DIO, DSO, DWC, CCC is increasing.
The "🔥" emoji indicates a value if it increases by more than or decreases, e.g.
- DPO, finished goods, work in process, raw materials, GPM, OPM is increasing.
- O/D, DIO, DSO, DWC, CCC is decreasing.
The order of the list depends on the day of each item, the more days more high.
Yield Curve InversionThe Yield Curve Inversion indicator is a tool designed to help traders and analysts visualize and interpret the dynamics between the US 10-year and 2-year Treasury yields. This indicator is particularly useful for identifying yield curve inversions, often seen as a precursor to economic recessions.
Features and Interpretations
Display Modes: Choose between "Spread Mode" to visualize the yield spread indicating normal (green) or inverted (red) curves, or "Both Yields Mode" to view both yields.
Yield Spread: A plotted difference between 10-year and 2-year yields, with a zero line marking inversion. A negative spread suggests potential economic downturns.
Color Coding: Green for a normal yield curve (10Y > 2Y) and red for an inverted curve (2Y > 10Y).
Legend: Provides quick reference to yield curve states for easier interpretation.
This indicator is for educational and informational purposes only. It should not be considered financial advice or a recommendation to buy or sell any financial instruments. Users should conduct their own research and consult with a financial advisor before making investment decisions. The creator of this indicator is not responsible for any financial losses incurred through its use.
SP500 RatiosThe "SP500 Ratios" indicator is a powerful tool developed for the TradingView platform, allowing users to access a variety of financial ratios and inflation-adjusted data related to the S&P 500 index. This indicator integrates with Nasdaq Data Link (formerly known as Quandl) to retrieve historical data, providing a comprehensive overview of key financial metrics associated with the S&P 500.
Key Features
Price to Sales Ratio: Quarterly ratio of price to sales (revenue) for the S&P 500.
Dividend Yield: Monthly dividend yield based on 12-month dividend per share.
Price Earnings Ratio (PE Ratio): Monthly price-to-earnings ratio based on trailing twelve-month reported earnings.
CAPE Ratio (Shiller PE Ratio): Monthly cyclically adjusted PE ratio, based on average inflation-adjusted earnings over the past ten years.
Earnings Yield: Monthly earnings yield, the inverse of the PE ratio.
Price to Book Ratio: Quarterly ratio of price to book value.
Inflation Adjusted S&P 500: Monthly S&P 500 level adjusted for inflation.
Revenue Per Share: Quarterly trailing twelve-month sales per share, not adjusted for inflation.
Earnings Per Share: Monthly real earnings per share, adjusted for inflation.
User Configuration
The indicator offers flexibility through user-configurable options. You can choose to display or hide each metric according to your analysis needs. Users can also adjust the line width for better visibility on the chart.
Visualization
The selected data is plotted on the chart with distinct colors for each metric, facilitating visual analysis. A dynamic legend table is also generated in the top-right corner of the chart, listing the currently displayed metrics with their associated colors.
This indicator is ideal for traders and analysts seeking detailed insights into the financial performance and valuations of the S&P 500, while benefiting from the customization flexibility offered by TradingView.
Advanced Keltner Channel/Oscillator [MyTradingCoder]This indicator combines a traditional Keltner Channel overlay with an oscillator, providing a comprehensive view of price action, trend, and momentum. The core of this indicator is its advanced ATR calculation, which uses statistical methods to provide a more robust measure of volatility.
Starting with the overlay component, the center line is created using a biquad low-pass filter applied to the chosen price source. This provides a smoother representation of price than a simple moving average. The upper and lower channel lines are then calculated using the statistically derived ATR, with an additional set of mid-lines between the center and outer lines. This creates a more nuanced view of price action within the channel.
The color coding of the center line provides an immediate visual cue of the current price momentum. As the price moves up relative to the ATR, the line shifts towards the bullish color, and vice versa for downward moves. This color gradient allows for quick assessment of the current market sentiment.
The oscillator component transforms the channel into a different perspective. It takes the price's position within the channel and maps it to either a normalized -100 to +100 scale or displays it in price units, depending on your settings. This oscillator essentially shows where the current price is in relation to the channel boundaries.
The oscillator includes two key lines: the main oscillator line and a signal line. The main line represents the current position within the channel, smoothed by an exponential moving average (EMA). The signal line is a further smoothed version of the oscillator line. The interaction between these two lines can provide trading signals, similar to how MACD is often used.
When the oscillator line crosses above the signal line, it might indicate bullish momentum, especially if this occurs in the lower half of the oscillator range. Conversely, the oscillator line crossing below the signal line could signal bearish momentum, particularly if it happens in the upper half of the range.
The oscillator's position relative to its own range is also informative. Values near the top of the range (close to 100 if normalized) suggest that price is near the upper Keltner Channel band, indicating potential overbought conditions. Values near the bottom of the range (close to -100 if normalized) suggest proximity to the lower band, potentially indicating oversold conditions.
One of the strengths of this indicator is how the overlay and oscillator work together. For example, if the price is touching the upper band on the overlay, you'd see the oscillator at or near its maximum value. This confluence of signals can provide stronger evidence of overbought conditions. Similarly, the oscillator hitting extremes can draw your attention to price action at the channel boundaries on the overlay.
The mid-lines on both the overlay and oscillator provide additional nuance. On the overlay, price action between the mid-line and outer line might suggest strong but not extreme momentum. On the oscillator, this would correspond to readings in the outer quartiles of the range.
The customizable visual settings allow you to adjust the indicator to your preferences. The glow effects and color coding can make it easier to quickly interpret the current market conditions at a glance.
Overlay Component:
The overlay displays Keltner Channel bands dynamically adapting to market conditions, providing clear visual cues for potential trend reversals, breakouts, and overbought/oversold zones.
The center line is a biquad low-pass filter applied to the chosen price source.
Upper and lower channel lines are calculated using a statistically derived ATR.
Includes mid-lines between the center and outer channel lines.
Color-coded based on price movement relative to the ATR.
Oscillator Component:
The oscillator component complements the overlay, highlighting momentum and potential turning points.
Normalized values make it easy to compare across different assets and timeframes.
Signal line crossovers generate potential buy/sell signals.
Advanced ATR Calculation:
Uses a unique method to compute ATR, incorporating concepts like root mean square (RMS) and z-score clamping.
Provides both an average and mode-based ATR value.
Customizable Visual Settings:
Adjustable colors for bullish and bearish moves, oscillator lines, and channel components.
Options for line width, transparency, and glow effects.
Ability to display overlay, oscillator, or both simultaneously.
Flexible Parameters:
Customizable inputs for channel width multiplier, ATR period, smoothing factors, and oscillator settings.
Adjustable Q factor for the biquad filter.
Key Advantages:
Advanced ATR Calculation: Utilizes a statistical method to generate ATR, ensuring greater responsiveness and accuracy in volatile markets.
Overlay and Oscillator: Provides a comprehensive view of price action, combining trend and momentum analysis.
Customizable: Adjust settings to fine-tune the indicator to your specific needs and trading style.
Visually Appealing: Clear and concise design for easy interpretation.
The ATR (Average True Range) in this indicator is derived using a sophisticated statistical method that differs from the traditional ATR calculation. It begins by calculating the True Range (TR) as the difference between the high and low of each bar. Instead of a simple moving average, it computes the Root Mean Square (RMS) of the TR over the specified period, giving more weight to larger price movements. The indicator then calculates a Z-score by dividing the TR by the RMS, which standardizes the TR relative to recent volatility. This Z-score is clamped to a maximum value (10 in this case) to prevent extreme outliers from skewing the results, and then rounded to a specified number of decimal places (2 in this script).
These rounded Z-scores are collected in an array, keeping track of how many times each value occurs. From this array, two key values are derived: the mode, which is the most frequently occurring Z-score, and the average, which is the weighted average of all Z-scores. These values are then scaled back to price units by multiplying by the RMS.
Now, let's examine how these values are used in the indicator. For the Keltner Channel lines, the mid lines (top and bottom) use the mode of the ATR, representing the most common volatility state. The max lines (top and bottom) use the average of the ATR, incorporating all volatility states, including less common but larger moves. By using the mode for the mid lines and the average for the max lines, the indicator provides a nuanced view of volatility. The mid lines represent the "typical" market state, while the max lines account for less frequent but significant price movements.
For the color coding of the center line, the mode of the ATR is used to normalize the price movement. The script calculates the difference between the current price and the price 'degree' bars ago (default is 2), and then divides this difference by the mode of the ATR. The resulting value is passed through an arctangent function and scaled to a 0-1 range. This scaled value is used to create a color gradient between the bearish and bullish colors.
Using the mode of the ATR for this color coding ensures that the color changes are based on the most typical volatility state of the market. This means that the color will change more quickly in low volatility environments and more slowly in high volatility environments, providing a consistent visual representation of price momentum relative to current market conditions.
Using a good IIR (Infinite Impulse Response) low-pass filter, such as the biquad filter implemented in this indicator, offers significant advantages over simpler moving averages like the EMA (Exponential Moving Average) or other basic moving averages.
At its core, an EMA is indeed a simple, single-pole IIR filter, but it has limitations in terms of its frequency response and phase delay characteristics. The biquad filter, on the other hand, is a two-pole, two-zero filter that provides superior control over the frequency response curve. This allows for a much sharper cutoff between the passband and stopband, meaning it can more effectively separate the signal (in this case, the underlying price trend) from the noise (short-term price fluctuations).
The improved frequency response of a well-designed biquad filter means it can achieve a better balance between smoothness and responsiveness. While an EMA might need a longer period to sufficiently smooth out price noise, potentially leading to more lag, a biquad filter can achieve similar or better smoothing with less lag. This is crucial in financial markets where timely information is vital for making trading decisions.
Moreover, the biquad filter allows for independent control of the cutoff frequency and the Q factor. The Q factor, in particular, is a powerful parameter that affects the filter's resonance at the cutoff frequency. By adjusting the Q factor, users can fine-tune the filter's behavior to suit different market conditions or trading styles. This level of control is simply not available with basic moving averages.
Another advantage of the biquad filter is its superior phase response. In the context of financial data, this translates to more consistent lag across different frequency components of the price action. This can lead to more reliable signals, especially when it comes to identifying trend changes or price reversals.
The computational efficiency of biquad filters is also worth noting. Despite their more complex mathematical foundation, biquad filters can be implemented very efficiently, often requiring only a few operations per sample. This makes them suitable for real-time applications and high-frequency trading scenarios.
Furthermore, the use of a more sophisticated filter like the biquad can help in reducing false signals. The improved noise rejection capabilities mean that minor price fluctuations are less likely to cause unnecessary crossovers or indicator movements, potentially leading to fewer false breakouts or reversal signals.
In the specific context of a Keltner Channel, using a biquad filter for the center line can provide a more stable and reliable basis for the entire indicator. It can help in better defining the overall trend, which is crucial since the Keltner Channel is often used for trend-following strategies. The smoother, yet more responsive center line can lead to more accurate channel boundaries, potentially improving the reliability of overbought/oversold signals and breakout indications.
In conclusion, this advanced Keltner Channel indicator represents a significant evolution in technical analysis tools, combining the power of traditional Keltner Channels with modern statistical methods and signal processing techniques. By integrating a sophisticated ATR calculation, a biquad low-pass filter, and a complementary oscillator component, this indicator offers traders a comprehensive and nuanced view of market dynamics.
The indicator's strength lies in its ability to adapt to varying market conditions, providing clear visual cues for trend identification, momentum assessment, and potential reversal points. The use of statistically derived ATR values for channel construction and the implementation of a biquad filter for the center line result in a more responsive and accurate representation of price action compared to traditional methods.
Furthermore, the dual nature of this indicator – functioning as both an overlay and an oscillator – allows traders to simultaneously analyze price trends and momentum from different perspectives. This multifaceted approach can lead to more informed decision-making and potentially more reliable trading signals.
The high degree of customization available in the indicator's settings enables traders to fine-tune its performance to suit their specific trading styles and market preferences. From adjustable visual elements to flexible parameter inputs, users can optimize the indicator for various trading scenarios and time frames.
Ultimately, while no indicator can predict market movements with certainty, this advanced Keltner Channel provides traders with a powerful tool for market analysis. By offering a more sophisticated approach to measuring volatility, trend, and momentum, it equips traders with valuable insights to navigate the complex world of financial markets. As with any trading tool, it should be used in conjunction with other forms of analysis and within a well-defined risk management framework to maximize its potential benefits.
Indian Markets Dashboard The Mobile Dashboard indicator provides a compact and customizable table on your TradingView chart, summarizing key data for up to six selected financial instruments. It displays the close price, previous day high (PDH), previous day low (PDL), and SuperTrend direction (Bull/Bear). The table's position, size, and transparency can be adjusted to suit your preferences, making it a convenient tool for quickly monitoring multiple assets on the go. Ideal for traders who need a clear and concise overview of market conditions directly on their chart.
- TraderVK
Qty CalculatorThis Pine Script indicator, titled "Qty Calculator," is a customizable tool designed to assist traders in managing their trades by calculating key metrics related to risk management. It takes into account your total capital, entry price, stop-loss level, and desired risk percentage to provide a comprehensive overview of potential trade outcomes.
Key Features:
User Inputs:
Total Capital: The total amount of money available for trading.
Entry Price: The price at which the trader enters the trade.
Stop Loss: The price level at which the trade will automatically close to prevent further losses.
Risk Percentage: The percentage of the total capital that the trader is willing to risk on a single trade.
Customizable Table:
Position: The indicator allows you to choose the position of the table on the chart, with options including top-left, top-center, top-right, bottom-left, bottom-center, and bottom-right.
Size: You can adjust the number of rows and columns in the table to fit your needs.
Risk Management Calculations:
Difference Calculation: The difference between the entry price and the stop-loss level.
Risk Per Trade: Calculated as a percentage of your total capital.
Risk Levels: The indicator evaluates multiple risk levels (0.10%, 0.25%, 0.50%, 1.00%) and calculates the quantity, capital per trade, percentage of total capital, and the risk amount associated with each level.
R-Multiples Calculation:
The indicator calculates potential profit levels at 2x, 3x, 4x, and 5x the risk (R-Multiples), showing the potential gains if the trade moves in your favor by these multiples.
Table Display:
The table includes the following columns:
CapRisk%: Displays the risk percentage.
Qty: The quantity of the asset you should trade.
Cap/Trade: The capital allocated per trade.
%OfCapital: The percentage of total capital used in the trade.
Risk Amount: The monetary risk taken on each trade.
R Gains: Displays potential gains at different R-Multiples.
This indicator is particularly useful for traders who prioritize risk management and want to ensure that their trades are aligned with their capital and risk tolerance. By providing a clear and customizable table of critical metrics, it helps traders make informed decisions and better manage their trading strategies.
Thrax - QuickStrike 5-Mins Scalping** Indicator Description **
1. Price Change Threshold (%) – The minimum price change required for a candle to be recognized as significant. Candles exceeding this threshold are considered potential candidates for zone creation. Default value for 5 min is 0.5%. As you move on higher timeframe the threshold should increase
2. Percentage Change for Zones (%) – The amount of price movement needed to form a dynamic support or resistance zone. Tweak this to control how sensitive the indicator is to price fluctuations. 5 min default value is 1%. For 15 min suggested is 2-3%.
3. Break Threshold for Zones (%) – Defines how much price must break above or below a zone for it to be removed from the chart/mitigated. Keeps the chart clean by removing invalidated zones. Default value is 0.1% in 5 min, for 15 min it is 0.5%.
4. Buy Zone Retracement Level (%) – The percentage retracement level for defining the inner buy zone within a broader bullish zone. Ideal for timing precision entries. Ideal value is 75%
5. Sell Zone Retracement Level (%) – The percentage retracement level used to determine the inner sell zone within a larger bearish zone. Helps in identifying potential reversal areas or exits. Ideal value is 25%
By tailoring these inputs, traders can fully customize the indicator to suit their scalping strategies, enhancing their ability to navigate fast-moving markets with confidence.
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There are two primary approaches for scalping using this indicator:
1. Candle-Based Scalping:
a. Bullish Signal: When you observe a bullish candle highlighted in blue (by default), you can consider entering a long position at the close of this candle. It’s advisable to wait for the candle to close before taking action. For a more aggressive scalp, you might take profits based on your scalp target after a few subsequent candles. If the price remains stagnant or moves unfavorably in the next few candles, you can exit with a small loss. Alternatively, if you have a higher risk tolerance, you may hold the position even if the price initially declines within a set percentage.
b. Bearish Signal: For a bearish candle highlighted in yellow, you can enter a short trade at the close of the candle. Similar to the bullish setup, you have the option to exit after a few candles if the price doesn’t move as expected or hold the position with a higher risk tolerance if the price goes up initially.
2. Zone-Based Scalping:
Entering Zones: Monitor the price as it enters a defined support or resistance zone. If you are open to higher risk, you can enter a trade immediately upon the price entering the zone. For a more cautious approach with a smaller stop loss, wait for the price to reach a retracement level within the zone before initiating your trade. This approach allows for a more precise entry but may result in missing out on trades if the price reverses before hitting the retracement level. Conversely, entering at the zone’s boundary offers the potential for early trade capture but comes with a higher stop loss risk.
Adjust these strategies based on your risk tolerance and trading preferences to optimize your scalping opportunities.
Trade ScoreboardManually track your trade record and display your results in a handy table. Good for backtesting and for making a manual ritual out of tracking your trades. This way you are less likely to overtrade during your session. This can also nice as an addendum to your charts, to show off your results and record them for later review.
S&P 2024: Magnificent 7 vs. the rest of S&PThis chart is designed to calculate and display the percentage change of the Magnificent 7 (M7) stocks and the S&P 500 excluding the M7 (Ex-M7) from the beginning of 2024 to the most recent data point. The Magnificent 7 consists of seven major technology stocks: Apple (AAPL), Microsoft (MSFT), Amazon (AMZN), Alphabet (GOOGL), Meta (META), Nvidia (NVDA), and Tesla (TSLA). These stocks are a significant part of the S&P 500 and can have a substantial impact on its overall performance.
Key Components and Functionality:
1. Start of 2024 Baseline:
- The script identifies the closing prices of the S&P 500 and each of the Magnificent 7 stocks on the first trading day of 2024. These values serve as the baseline for calculating percentage changes.
2. Current Value Calculation:
- It then fetches the most recent closing prices of these stocks and the S&P 500 index to calculate their current values.
3. Percentage Change Calculation:
- The script calculates the percentage change for the M7 by comparing the sum of the current prices of the M7 stocks to their combined value at the start of 2024.
- Similarly, it calculates the percentage change for the Ex-M7 by comparing the current value of the S&P 500 excluding the M7 to its value at the start of 2024.
4. Plotting:
- The calculated percentage changes are plotted on the chart, with the M7’s percentage change shown in red and the Ex-M7’s percentage change shown in blue.
Use Case:
This indicator is particularly useful for investors and analysts who want to understand how much the performance of the S&P 500 in 2024 is driven by the Magnificent 7 stocks compared to the rest of the index. By showing the percentage change from the start of the year, it provides clear insights into the relative growth or decline of these two segments of the market over the course of the year.
Visualization:
- Red Line (M7 % Change): Displays the percentage change of the combined value of the Magnificent 7 stocks since the start of 2024.
- Blue Line (Ex-M7 % Change): Displays the percentage change of the S&P 500 excluding the Magnificent 7 since the start of 2024.
This script enables a straightforward comparison of the performance of the M7 and Ex-M7, highlighting which segment is contributing more to the overall movement of the S&P 500 in 2024.
3-Criteria StrategyThe "3-Criteria Strategy" is a simple yet effective trading strategy based on three criteria:
200-Day Moving Average: The first criterion checks whether the current price is above or below the 200-day moving average (SMA). A price above the 200-day line is considered bullish (thumbs up), while a price below is considered bearish (thumbs down).
5-Day Indicator: The second criterion evaluates the performance of the first five trading days of the year. If the closing price on the fifth trading day is higher than the closing price on the last trading day of the previous year, this is considered bullish (thumbs up). Otherwise, it's bearish (thumbs down).
Year-to-Date (YTD) Effect: The third criterion compares the current price with the closing price at the end of the previous year. A current price above the year-end price is bullish (thumbs up), while a price below is bearish (thumbs down).
Signal Interpretation:
Buy Signal: At least two of the three criteria must give a bullish signal (thumbs up).
Sell Signal: Zero or one bullish signal results in a bearish outlook.
The script provides visual cues with background colors:
Green background: Indicates a buy signal.
Red background: Indicates a sell signal.
Additionally, the script plots the 200-day moving average and the YTD line on the chart for better visualization.
Usage:
Apply the Script: Add the script to your TradingView chart.
Interpret Signals: Monitor the background color and the status label to determine trading actions.
Visual Aids: Use the 200-day line and YTD line plotted on the chart to confirm the criteria visually.
Scientific Research
The concepts used in this script—like the 200-day moving average and Year-to-Date effects—are well-documented in financial literature. However, the combination of these specific criteria as a trading strategy is more of a heuristic approach commonly used by traders rather than a subject of extensive academic research.
200-Day Moving Average: The 200-day moving average is widely regarded as a significant level in technical analysis, often serving as a demarcation between long-term bullish and bearish trends. Research has shown that long-term moving averages can be useful for trend-following strategies.
Reference: Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance, 47(5), 1731-1764.
Year-to-Date and Calendar Effects: The Year-to-Date effect and early-year performance (such as the January effect) have been studied extensively in the context of seasonal market anomalies.
Reference: Rozeff, M. S., & Kinney, W. R. (1976). Capital Market Seasonality: The Case of Stock Returns. Journal of Financial Economics, 3(4), 379-402.
While these papers don't address the exact combination of criteria used in your strategy, they provide a solid foundation for understanding the underlying concepts.
Cumulative Gain/Loss Histogram This TradingView Pine Script indicator combines several analytical tools to assist traders in making informed investment decisions. It calculates and visualizes cumulative gain/loss percentage, standard deviation levels, and normalizes trading volume on a reversed scale.
Components:
Basis for Calculation:
Users can select the basis data for the calculations: Price, VIX (Volatility Index), VVIX (Volatility of Volatility Index), or MOVE (Volatility Index for Treasury Securities).
Cumulative Gain/Loss:
This is computed based on the selected basis. The script tracks the cumulative percentage change in the selected basis data. Positive changes are aggregated to track gains, while negative changes accumulate to track losses.
Standard Deviation Levels:
The script calculates standard deviation (StdDev) for the cumulative gain/loss data over a specified period. Two levels are determined:
Positive StdDev Level: Shows the upper threshold for gains.
Negative StdDev Level: Shows the lower threshold for losses.
These levels are useful for identifying extreme deviations in the data.
Normalized Volume:
The trading volume is normalized to fit within a -5 to 5 scale, but the scale is reversed. Higher trading volumes will be represented by lower values on this scale. This normalized volume is plotted as a gray line on the chart.
How to Use This Indicator:
Identify Trends and Extremes:
Cumulative Gain/Loss: Look for periods where the cumulative gain/loss exceeds the standard deviation levels. This can indicate significant trend changes or potential reversals. Standard Deviation Levels: Use these levels to gauge whether the market is experiencing extreme conditions. For example, if the cumulative gain/loss crosses above the positive StdDev level, it might suggest an overbought condition.
Volume Analysis:
Normalized Volume: Analyze the volume trends with the reversed scale. Higher normalized volume values (which are lower on the -5 to 5 scale) could indicate high trading activity or market interest, potentially signaling a strong move or trend. Conversely, lower normalized volume values (which are higher on the -5 to 5 scale) may suggest lower trading activity or consolidation.
Decision-Making:
Combine the insights from cumulative gain/loss and standard deviation levels with volume analysis to make more informed trading decisions.
Buy Signal: Consider entering a position when the cumulative gain/loss reaches or exceeds the negative StdDev level and volume analysis supports increased market activity.
Sell Signal: Consider exiting a position when the cumulative gain/loss exceeds the positive StdDev level, indicating possible overbought conditions, especially if volume trends also align with the potential reversal.
Summary:
This script is designed to help traders understand market dynamics through cumulative gain/loss trends, standard deviation thresholds, and volume analysis. By interpreting these elements together, traders can identify potential trading opportunities and make more informed decisions based on market conditions and trends.
Correlation Clusters [LuxAlgo]The Correlation Clusters is a machine learning tool that allows traders to group sets of tickers with a similar correlation coefficient to a user-set reference ticker.
The tool calculates the correlation coefficients between 10 user-set tickers and a user-set reference ticker, with the possibility of forming up to 10 clusters.
🔶 USAGE
Applying clustering methods to correlation analysis allows traders to quickly identify which set of tickers are correlated with a reference ticker, rather than having to look at them one by one or using a more tedious approach such as correlation matrices.
Tickers belonging to a cluster may also be more likely to have a higher mutual correlation. The image above shows the detailed parts of the Correlation Clusters tool.
The correlation coefficient between two assets allows traders to see how these assets behave in relation to each other. It can take values between +1.0 and -1.0 with the following meaning
Value near +1.0: Both assets behave in a similar way, moving up or down at the same time
Value close to 0.0: No correlation, both assets behave independently
Value near -1.0: Both assets have opposite behavior when one moves up the other moves down, and vice versa
There is a wide range of trading strategies that make use of correlation coefficients between assets, some examples are:
Pair Trading: Traders may wish to take advantage of divergences in the price movements of highly positively correlated assets; even highly positively correlated assets do not always move in the same direction; when assets with a correlation close to +1.0 diverge in their behavior, traders may see this as an opportunity to buy one and sell the other in the expectation that the assets will return to the likely same price behavior.
Sector rotation: Traders may want to favor some sectors that are expected to perform in the next cycle, tracking the correlation between different sectors and between the sector and the overall market.
Diversification: Traders can aim to have a diversified portfolio of uncorrelated assets. From a risk management perspective, it is useful to know the correlation between the assets in your portfolio, if you hold equal positions in positively correlated assets, your risk is tilted in the same direction, so if the assets move against you, your risk is doubled. You can avoid this increased risk by choosing uncorrelated assets so that they move independently.
Hedging: Traders may want to hedge positions with correlated assets, from a hedging perspective, if you are long an asset, you can hedge going long a negatively correlated asset or going short a positively correlated asset.
Grouping different assets with similar behavior can be very helpful to traders to avoid over-exposure to those assets, traders may have multiple long positions on different assets as a way of minimizing overall risk when in reality if those assets are part of the same cluster traders are maximizing their risk by taking positions on assets with the same behavior.
As a rule of thumb, a trader can minimize risk via diversification by taking positions on assets with no correlations, the proposed tool can effectively show a set of uncorrelated candidates from the reference ticker if one or more clusters centroids are located near 0.
🔶 DETAILS
K-means clustering is a popular machine-learning algorithm that finds observations in a data set that are similar to each other and places them in a group.
The process starts by randomly assigning each data point to an initial group and calculating the centroid for each. A centroid is the center of the group. K-means clustering forms the groups in such a way that the variances between the data points and the centroid of the cluster are minimized.
It's an unsupervised method because it starts without labels and then forms and labels groups itself.
🔹 Execution Window
In the image above we can see how different execution windows provide different correlation coefficients, informing traders of the different behavior of the same assets over different time periods.
Users can filter the data used to calculate correlations by number of bars, by time, or not at all, using all available data. For example, if the chart timeframe is 15m, traders may want to know how different assets behave over the last 7 days (one week), or for an hourly chart set an execution window of one month, or one year for a daily chart. The default setting is to use data from the last 50 bars.
🔹 Clusters
On this graph, we can see different clusters for the same data. The clusters are identified by different colors and the dotted lines show the centroids of each cluster.
Traders can select up to 10 clusters, however, do note that selecting 10 clusters can lead to only 4 or 5 returned clusters, this is caused by the machine learning algorithm not detecting any more data points deviating from already detected clusters.
Traders can fine-tune the algorithm by changing the 'Cluster Threshold' and 'Max Iterations' settings, but if you are not familiar with them we advise you not to change these settings, the defaults can work fine for the application of this tool.
🔹 Correlations
Different correlations mean different behaviors respecting the same asset, as we can see in the chart above.
All correlations are found against the same asset, traders can use the chart ticker or manually set one of their choices from the settings panel. Then they can select the 10 tickers to be used to find the correlation coefficients, which can be useful to analyze how different types of assets behave against the same asset.
🔶 SETTINGS
Execution Window Mode: Choose how the tool collects data, filter data by number of bars, time, or no filtering at all, using all available data.
Execute on Last X Bars: Number of bars for data collection when the 'Bars' execution window mode is active.
Execute on Last: Time window for data collection when the `Time` execution window mode is active. These are full periods, so `Day` means the last 24 hours, `Week` means the last 7 days, and so on.
🔹 Clusters
Number of Clusters: Number of clusters to detect up to 10. Only clusters with data points are displayed.
Cluster Threshold: Number used to compare a new centroid within the same cluster. The lower the number, the more accurate the centroid will be.
Max Iterations: Maximum number of calculations to detect a cluster. A high value may lead to a timeout runtime error (loop takes too long).
🔹 Ticker of Reference
Use Chart Ticker as Reference: Enable/disable the use of the current chart ticker to get the correlation against all other tickers selected by the user.
Custom Ticker: Custom ticker to get the correlation against all the other tickers selected by the user.
🔹 Correlation Tickers
Select the 10 tickers for which you wish to obtain the correlation against the reference ticker.
🔹 Style
Text Size: Select the size of the text to be displayed.
Display Size: Select the size of the correlation chart to be displayed, up to 500 bars.
Box Height: Select the height of the boxes to be displayed. A high height will cause overlapping if the boxes are close together.
Clusters Colors: Choose a custom colour for each cluster.
Reward Ratio ValidatorThis PineScript code creates an indicator called "Reward Ratio Validator" that helps traders evaluate potential trade setups based on pivot points, standard deviation, and risk/reward ratios. Here's a breakdown of what the code does:
1. Input parameters:
- Pivot: Number of bars for pivot calculation
- STDEV Length: Number of bars for standard deviation calculation
- Risk / Reward: The desired risk-to-reward ratio
- STDEV Multiplier: Multiplier for the standard deviation
- On : Short | Off : Long: A toggle to switch between short and long trade analysis
2. Pivot point calculation:
- The code calculates pivot highs and lows using the specified pivot length
- It stores the last pivot high and low in an array
3. Standard deviation calculation:
- Calculates the standard deviation of closing prices over the specified length
4. Risk/Reward deviation calculation:
- For long trades (when show is false):
Calculates the price level where the reward would be 'rvr' times the risk, based on the last pivot low
- For short trades (when show is true):
Calculates the price level where the reward would be 'rvr' times the risk, based on the last pivot high
5. Plotting:
- Plots the calculated risk/reward levels for both long and short trades
- Plots the multiplied standard deviation
6. Visual representation:
- Fills the area between the risk/reward levels and the standard deviation plot
- Uses color coding to indicate whether the current price movement exceeds the standard deviation threshold:
- Green: The move is within the standard deviation threshold
- Red: The move exceeds the standard deviation threshold
This indicator helps traders visually assess whether a potential trade setup offers the desired risk/reward ratio while considering the recent price volatility (represented by the standard deviation). It can be used to identify possible entry points for both long and short trades that meet specific risk/reward criteria.
Monte Carlo (Polyline Traceback) [Kioseff Trading]Hello!
This script "Monte Carlo (Polyline Traceback) " performs a Monte Carlo simulation using polylines!
By using polylines, and tracing back the initial simulation to its origin point, we can better replicate the ideal output of a Monte Carlo simulation!
Such as:
The image above shows the output of a simulation (image sourced outside TV).
With this script, and polyline capabilities, we can come quite close on TradingView.
The image above shows the indicator in action! Not bad considering the ideal output.
Of course, the script is quite heavy and tries its best to circumvent limitations :D
You might run into load time errors, in which case you might try applying the built-in setting "Force Script Load". This setting will cut-off the visuals for some simulations, but has a higher chance of passing load-time limitations!
As shown in the image above, you can select to only show worst-case and best-case simulations. Using this option will reduce chart lag and improve load times.
Features
Monte Carlo Simulation: Performs Monte Carlo simulation to generate multiple future paths.
Asset Price: Can simulate future asset prices based on historical log returns.
Statistical Methods: Offers two simulation methods—Gaussian (Normal) distribution and Bootstrapping.
Adjustable Parameters: Offers numerous user-adjustable settings like number of simulations, forecast length, and more.
Historical Data Points: Option to specify the amount of historical data to be used in the simulation (price).
Best/Worst Case: Allows you to show only the best case / worst case outcome (range) for all simulations!
Thank you!
