Bitcoin Destiny Line Model v1.1The Bitcoin Destiny Line Model
Table of Contents
1. Overview
2. Analytical and Technical Techniques Employed
3. Objectives of the Bitcoin Destiny Line Model
4. Key Technical Components and Functionalities
4.1. Bitcoin Destiny Line and Heatmap
4.2. Halving Cycles Markers
4.3. Dynamic Repricing Rails with Diminishing Volatility Adjustment
4.4. Seasonal Dynamics
4.5. Support and Resistance Zones
4.6. Market Action Indicators
4.7. Cycle Projections
4.8. Heatmap Only
5. Settings
6. Different Strategies to Utilize the Model
6.1. Value-Based Entry Strategy
6.2. Long-Term Position Strategy
6.3. Scaling Out Strategy
6.4. Portfolio Rebalancing Strategy
6.5. Bear Market Strategy
6.6. Short-Term Trading Strategy
7. Recommendations and Disclosures
1. Overview
The Bitcoin Destiny Line Model is a technical analysis toolset designed exclusively for Bitcoin. It integrates a comprehensive suite of analytical methodologies to provide deep insights into Bitcoin's market dynamics focusing on long-term investment strategies.
By analyzing historical data through various technical frameworks, the model helps investors gain insight into the current market structure, cycle dynamics, direction, and trend of Bitcoin, assisting investors and traders with data-driven decision-making.
2. Analytical and Technical Techniques Employed
The model integrates a range of analytical techniques:
Cycle Analysis - Centers on the Bitcoin halving event to anticipate phases within the Bitcoin cycle.
Logarithmic Regression Analysis - Calculates the logarithmic growth of Bitcoin over time.
Standard Deviation - Measures how significantly the price action differs from the long-term logarithmic trend.
Fibonacci Analysis - Identifies support and resistance levels.
Multi-Timeframe Momentum - Analyzes overbought or oversold conditions across multiple periods.
Trendlines - Draws trendlines from expected cycle lows to expected cycle highs extending logarithmic and deviation lines into the future as projection lines.
3. Objectives of the Bitcoin Destiny Line Model
The model is crafted to deliver an empirical framework for Bitcoin investing:
Bitcoin Market Structure - Offers insights into Bitcoin’s market structure.
Identify Value Opportunities and Risk Areas - Pinpoints potential value-entry opportunities and recognizes when the market is over-extended.
Leverage Market Cycles - Utilizes knowledge of Bitcoin’s seasonal dynamics and halving cycles to inform investment strategies.
Mitigate Downside Risk - Provides indicators for potential market corrections, aiding in risk management and avoidance of buying at peak prices.
4. Key Technical Components and Functionalities
4.1. Bitcoin Destiny Line and Heatmap
The cycle low to cycle high line with a risk-based color-coded heatmap serves as a central reference for Bitcoin’s price trajectory.
It emphasizes the long-term trend indicating areas of value in cool colors and areas of risk in warm colors.
4.2. Halving Cycles Markers
Bitcoin halving events are marked on the chart with vertical lines forming anchor points for cycle analysis.
4.3. Dynamic Repricing Rails with Diminishing Volatility Adjustment
Repricing rails based on the long-term logarithmic trend highlight the rails on which Bitcoin's price will reprice up or down.
Adjusts to the diminishing volatility of the asset over time as it matures.
4.4. Seasonal Dynamics
Integrates Bitcoin's inherent seasonal trends to provide additional context for market conditions aligning with broader market analysis.
Understanding Bitcoin’s seasons:
Spring Awakening - The initial recovery phase where the market begins to rebound from a bear market showing early signs of improvement. This is an ideal time for cautious optimism. Investors should consider gradually increasing their positions in Bitcoin, focusing on accumulation as confidence in market recovery grows.
Blossom Boom - A market bottom has been confirmed by now and market interest continues to pick up ahead of the Bitcoin halving. This typically presents a great opportunity for investors to position themselves advantageously ahead of expected price movements. It’s a good time to review and adjust portfolios to align with anticipated trends.
Midsummer Momentum - This phase follows the Bitcoin halving, characterized by a sideways to upward price trend often supported by heightened interest and media coverage. It represents potentially the last opportunity in the cycle for investors to purchase Bitcoin at lower price levels unlikely to be seen again. Investors should closely monitor the market for value buying opportunities to bolster their long-term investment strategies.
Rocket Rise - A phase where Bitcoin prices are likely to surge dramatically driven by a mix of Fear of Missing Out (FOMO) among new investors and widespread media hype. The strategy here is twofold: long-term holders should hold steady to reap maximum gains whereas more speculative investors might look to capitalize on the volatility by taking profits at optimal moments before a potential correction.
Winter Whispers - Following a bull run, the market begins to cool, marked by some investors taking profits and consequently increasing price fluctuations and volatility. During this time, investors should remain vigilant, tightening stop-loss orders to safeguard gains. This phase may be suitable for those looking to liquidate a portion of their long-term investments. However, for an investor to be selling the majority of their Bitcoin holdings is generally not advisable as it could preclude benefiting from potential future appreciations.
Deep Freeze - The market enters a bearish phase with significant price declines and market corrections. It's a period of consolidation and resetting of price levels. The end of this stage could typically be seen as a buying opportunity for the long-term investor. Accumulating Bitcoin during this phase can be advantageous as prices are lower and provide a foundation for significant growth in the next cycle.
4.5. Support and Resistance Zones
Calculates key levels that inform stop-loss placements and trading size decisions enhancing trading strategy around the Bitcoin Destiny Line.
4.6. Market Action Indicators
Suggests potential trading actions for different market phases aiding traders in identifying investment/trading opportunities.
Risk Indicator - Signals when prices are extremely over-extended helping to avoid entries during potential peak valuations.
4.7. Cycle Projections
Extends repricing levels into the future providing a visual forecast of expected price movements and enhancing strategic planning capabilities.
Cycle-High Price Projection Range - Provides a probabilistic range for upcoming cycle peaks based on historical trends and current market analysis.
4.8. Heatmap Only
It is also possible to plot the heatmap only as a background or as a bar in a second indicator.
4.9. Complete Visual View
A complete view of all key elements switched on the model.
5. Settings
Users can select to only show specific elements or all elements of the model.
They can set the sensitivity of some of the model elements and adjust certain view settings.
6. Different Strategies to Utilize the Model
The following strategies are enabled by the Bitcoin Destiny Line model:
6.1. Value-Based Entry Strategy
Investors can optimize their investment strategy by deploying investable cash either as a lump sum or on a dollar-cost averaging basis upon the display of a value indicator (Up-Triangles) which signals the highest probability for value entries.
6.2. Long-Term Position Strategy
As an alternative, investors may prefer to continue deploying investable funds while cooler colors (green or blue) are displayed on the value map, indicating favorable conditions for long-term positions.
6.3. Scaling Out Strategy
Investors may choose to scale out some of their investment upon the display of a risk indicator (circles) reducing exposure to potential downturns.
6.4. Portfolio Rebalancing Strategy
A sound strategy can also be to follow a portfolio rebalancing approach by deploying available investable cash upon the display of a value indicator. Rebalance the portfolio to maintain 25% in cash upon the display of a risk indicator. Adjust this ratio as subsequent risk indicators are triggered, deploying available cash upon future value signals.
6.5. Bear Market Strategy
In a bear market, traders may seek short positions upon the display of the Continued Downward Momentum indicator (Down Triangles) capitalizing on declining market trends.
6.6. Short-Term Trading Strategy
Traders can use hourly or 4-hourly data along with the daily Price Rails and Heatmap Bar for short-term positions. They may incorporate other preferred indicators such as RSI for entry/exit decisions.
7. Recommendations and Disclosures
Investors are recommended to take a prudent approach. It is not recommended for investors to scale out completely or significantly reduce the largest portion of their long-term Bitcoin positions in hopes of buying back at lower prices unless they have a compelling reason to do so. The future market conditions may not replicate past opportunities making this strategy uncertain. However, scaling out a smaller portion such as 25% can offer a high potential for an asymmetric risk-reward ratio. This approach is likely to provide a higher risk-adjusted return compared to traditional dollar-cost averaging or random lump sum adjustments.
The Bitcoin Destiny Line Model leverages 13.5 years of available price data across four complete Bitcoin market cycles.
While each additional cycle enriches the model's robustness and enhances the reliability of its forecasts, it is crucial for users to understand that historical trends are indicative of probable future directions and potential price ranges. Users should be cognizant that past performance is not a definitive predictor of future results and should not be the sole basis for investment decisions.
Regressions
Moving Average Cross Probability [AlgoAlpha]Moving Average Cross Probability 📈✨
The Moving Average Cross Probability by AlgoAlpha calculates the probability of a cross-over or cross-under between the fast and slow values of a user defined Moving Average type before it happens, allowing users to benefit by front running the market.
✨ Key Features:
📊 Probability Histogram: Displays the Probability of MA cross in the form of a histogram.
🔄 Data Table: Displays forecast information for quick analysis.
🎨 Customizable MAs: Choose from various moving averages and customize their length.
🚀 How to Use:
🛠 Add Indicator: Add the indicator to favorites, and customize the settings to suite your trading style.
📊 Analyze Market: Watch the indicator to look for trend shifts early or for trend continuations.
🔔 Set Alerts: Get notified of bullish/bearish points.
✨ How It Works:
The Moving Average Cross Probability Indicator by AlgoAlpha determines the probability by looking at a probable range of values that the price can take in the next bar and finds out what percentage of those possibilities result in the user defined moving average crossing each other. This is done by first using the HMA to predict what the next price value will be, a standard deviation based range is then calculated. The range is divided by the user defined resolution and is split into multiple levels, each of these levels represent a possible value for price in the next bar. These possible predicted values are used to calculate the possible MA values for both the fast and slow MAs that may occur in the next bar and are then compared to see how many of those possible MA results end up crossing each other.
Stay ahead of the market with the Moving Average Cross Probability Indicator AlgoAlpha! 📈💡
Cipher Mean ReversionThe Cipher Mean Reversion Indicator is an advanced trading tool that dynamically adjusts to market volatility to provide optimal entry and exit signals. This indicator is designed to identify significant deviations from a calculated mean, signaling potential reversal points where prices might revert to their average.
Core Functionality:
Cipher Mean Reversion uses an Exponential Moving Average (EMA) as the foundation for its mean price calculation. What sets Cipher apart is its dynamic adjustment mechanism that adapts the sensitivity of the EMA based on a volatility index. This index assesses both the rate and magnitude of price changes over a user-specified period, utilizing standard deviation and average true range calculations to gauge market volatility.
Unique Features:
Dynamic Sensitivity Adjustment: The sensitivity of our mean reversion detection changes in real-time, driven by our proprietary volatility index. This index is calculated using a combination of standard deviation and average true range, providing a robust measure of market volatility that informs the adjustment of our signal thresholds.
Adaptive Signal Thresholds: Instead of static buy and sell thresholds, Cipher uses thresholds that adapt to ongoing market conditions. These thresholds expand during periods of high volatility to reduce the risk of false signals and contract during quieter market conditions to capture smaller price reversals.
Signal Generation:
Buy Signals: Generated when the price falls significantly below the dynamically adjusted lower threshold, indicating an oversold condition ripe for reversal.
Sell Signals: Occur when the price exceeds the dynamically adjusted upper threshold, suggesting an overbought condition likely to revert.
Usage Tips:
Parameter Customization: Users can adjust the lookback period for the volatility assessment and the length of the EMA to better fit different assets and trading styles.
Complementary Analysis: For enhanced trading decisions, combine the Cipher Mean Reversion with other analytical tools such as volume indicators or momentum oscillators.
Risk Management: Employ risk management strategies, including predefined stop-loss and take-profit levels, tailored to the volatility insights provided by the indicator.
Originality and Usefulness:
The Cipher Mean Reversion Indicator offers a novel approach to mean reversion analysis by integrating real-time volatility adaptations into the signal generation process. This methodology ensures that the indicator remains highly responsive to changing market dynamics, providing traders with signals that are both timely and relevant.
Intended Use:
Cipher is versatile and can be used across various asset classes, including stocks, forex, and commodities. It is ideal for traders who require an indicator that can adapt to different market environments, from fast-moving markets to more stable conditions.
Cosine Kernel Regressions [QuantraSystems]Cosine Kernel Regressions
Introduction
The Cosine Kernel Regressions indicator (CKR) uses mathematical concepts to offer a unique approach to market analysis. This indicator employs Kernel Regressions using bespoke tunable Cosine functions in order to smoothly interpret a variety of market data, providing traders with incredibly clean insights into market trends.
The CKR is particularly useful for traders looking to understand underlying trends without the 'noise' typical in raw price movements. It can serve as a standalone trend analysis tool or be combined with other indicators for more robust trading strategies.
Legend
Fast Trend Signal Line - This is the foreground oscillator, it is colored upon the earliest confirmation of a change in trend direction.
Slow Trend Signal Line - This oscillator is calculated in a similar manner. However, it utilizes a lower frequency within the cosine tuning function, allowing it to capture longer and broader trends in one signal. This allows for tactical trading; the user can trade smaller moves without losing sight of the broader trend.
Case Study
In this case study, the CKR was used alongside the Triple Confirmation Kernel Regression Oscillator (KRO)
Initially, the KRO indicated an oversold condition, which could be interpreted as a signal to enter a long position in anticipation of a price rebound. However, the CKR’s fast trend signal line had not yet confirmed a positive trend direction - suggesting that entering a trade too early and without confirmation could be a mistake.
Waiting for a confirmed positive trend from the CKR proved beneficial for this trade. A few candles after the oversold signal, the CKR's fast trend signal line shifted upwards, indicating a strong upward momentum. This was the optimal entry point suggested by the CKR, occurring after the confirmation of the trend change, which significantly reduced the likelihood of entering during a false recovery or continuation of the downtrend.
This is one of the many uses of the CKR - by timing entries using the fast signal line , traders could avoid unnecessary losses by preventing premature entries.
Methodology
The methodology behind CKR is a multi-layered approach and utilizes many ‘base’ indicators.
Relative Strength Index
Stochastic Oscillator
Bollinger Band Percent
Chande Momentum Oscillator
Commodity Channel Index
Fisher Transform
Volume Zone Oscillator
The calculated output from each indicator is standardized and scaled before being averaged. This prevents any single indicator from overpowering the resulting signal.
// ╔════════════════════════════════╗ //
// ║ Scaling/Range Adjustment ║ //
// ╚════════════════════════════════╝ //
RSI_ReScale (_res ) => ( _res - 50 ) * 2.8
STOCH_ReScale (_stoch ) => ( _stoch - 50 ) * 2
BBPCT_ReScale (_bbpct ) => ( _bbpct - 0.5 ) * 120
CMO_ReScale (_chandeMO ) => ( _chandeMO * 1.15 )
CCI_ReScale (_cci ) => ( _cci / 2 )
FISH_ReScale (_fish1 ) => ( _fish1 * 30 )
VZO_ReScale (_VP, _TV ) => (_VP / _TV) * 110
These outputs are then fed into a customized cosine kernel regression function, which smooths the data, and combines all inputs into a single coherent output.
// ╔════════════════════════════════╗ //
// ║ COSINE KERNEL REGRESSIONS ║ //
// ╚════════════════════════════════╝ //
// Define a function to compute the cosine of an input scaled by a frequency tuner
cosine(x, z) =>
// Where x = source input
// y = function output
// z = frequency tuner
var y = 0.
y := math.cos(z * x)
Y
// Define a kernel that utilizes the cosine function
kernel(x, z) =>
var y = 0.
y := cosine(x, z)
math.abs(x) <= math.pi/(2 * z) ? math.abs(y) : 0. // cos(zx) = 0
// The above restricts the wave to positive values // when x = π / 2z
The tuning of the regression is adjustable, allowing users to fine-tune the sensitivity and responsiveness of the indicator to match specific trading strategies or market conditions. This robust methodology ensures that CKR provides a reliable and adaptable tool for market analysis.
Linear Regression InterceptLinear Regression Intercept (LRI) is a statistical method used to forecast future values based on past data. Financial markets frequently employ it to identify the underlying trend and determine when prices are overextended. Linear regression utilizes the least squares method to create a trendline by minimizing the distance between observed price data and the line. The LRI indicator calculates the intercept of this trendline for each data point, providing insights into price trends and potential trading opportunities.
Calculation and Interpretation of the LRI
The linear regression intercept is calculated using the following formula:
LRI = Y - (b * X)
Where Y represents the dependent variable (price), b is the slope of the regression line, and X is the independent variable (time). To determine the slope b, you can use the formula:
b = Σ / Σ(X - X_mean)^2
Once you have computed the LRI, it can be interpreted as the point at which the regression line intersects the Y-axis (price) when the independent variable (time) is zero. A positive LRI value indicates an upward trend, while a negative value suggests a downward trend. Traders can adjust the parameters of the LRI by modifying the period over which the linear regression is computed, which can impact the indicator’s sensitivity to recent price changes.
How to Use the LRI in Trading
To effectively use the LRI in trading, traders should consider the following:
Understanding the signals generated by the technical indicator: A rising LRI suggests an upward trend, whereas a falling LRI indicates a downward trend. Traders may use this information to help determine the market’s direction and identify reversals.
Combining the technical indicator with other indicators: The LRI can be used in conjunction with other technical indicators, such as moving averages, the Relative Strength Index (RSI), or traditional linear regression lines, to obtain a more comprehensive view of the market. In the case of traditional linear regression lines, the LRI helps traders identify the starting point of the trend, providing additional context to the overall trend direction.
Using the technical indicator for entry and exit signals: When the LRI crosses above or below a specific threshold, traders may consider it a potential entry or exit point. For example, if the LRI crosses above zero, it might signal a possible buying opportunity.
Linear Regression Trendline - Log, R-Squared, Dynamic RangeDescription:
This Pine Script tool is specifically crafted for in-depth technical analysis, integrating a logarithmic regression trendline with standard deviation (STDV) channel bands and the R-squared coefficient of determination. This sophisticated tool is tailored to provide a nuanced perspective on trend dynamics and volatility, particularly suitable for markets where changes are exponential rather than linear.
Key Features:
Logarithmic Regression Trendline: Uniquely employs a logarithmic approach to regression analysis, ideal for data that grows exponentially. This method emphasizes proportional changes and offers a more accurate fit for certain types of financial data.
STDV Channel Bands: Incorporates channel bands set at one or more standard deviations from the regression line. These bands offer insights into the volatility and relative price movements, aiding in the identification of potential buy and sell zones.
R-squared Coefficient: This tool differentiates itself by focusing on the R-squared coefficient of determination rather than Pearson's correlation coefficient. The R-squared value measures the proportion of variance in the dependent variable that is predictable from the independent variable, offering a more precise evaluation of the trendline’s effectiveness.
Flexible Period Settings: Unlike traditional tools, this script allows users to specify exact start and end points for the trendline analysis, either through direct date selection or by choosing specific bars. This flexibility facilitates precise modifications and adaptations to various analytical needs.
Interactive Usability: Features interactive capabilities allowing users to manually adjust the coordinates of the trendline’s start and end points during active sessions. This feature ensures that analysts can dynamically respond to market movements and adjust their analyses in real time.
Logarithmic Scaling: Specifically designed for logarithmic scaling, this tool is adept at handling data where growth rates are multiplicative, making it exceptionally useful in sectors like cryptocurrencies and rapidly growing stocks.
Usage:
This tool is ideal for traders and financial analysts who deal with high growth markets or any datasets where growth is exponential rather than linear. The focus on the R-squared coefficient enhances its utility by providing a critical assessment tool for evaluating the predictive strength and reliability of trends under logarithmic transformations.
Support and Resistance Polynomial Regressions | Flux ChartsOverview
This script is a dynamic form of support and resistance. Support and resistance plots areas where price commonly reverses its direction or “pivots”. A resistance line for instance is typically found by locating a price point where multiple high pivots occur. A high pivot is where a price increases for a number of bars then decreases for a number of bars creating a local maximum. This script takes the high pivots points but rather than using a horizontal line a polynomial regressed line is used.
It is common to see consecutive higher highs or lower lows or a mixed pattern of both so a classical support or resistance line can be insufficient. This script lets users find a polynomial of best fit for high pivots and low pivots creating a resistance and support line respectively.
Here are the same two sets of high and low pivots the first using linear regressed support and resistance lines the second using quadratic.
Here are the predicted results:
The Quadratic regression gives a much more accurate prediction of future pivot areas and the increase in variance of the data.
Quick Start
Add the script to the chart. Then select a left point and right point on the chart. This will be the data the script uses to calculate a best fit resistance line. Then select another left and right point that will be for the support line.
Now you can confirm your basic settings like the type of regression: Linear Regression, Quadratic Regression, Cubic Regression or Custom Regression.
After confirming the lines will be plotted on the graph.
Custom Polynomial Regression Setting
Polynomials follow the form:
The degree of a polynomial is the highest exponent in the equation. For example the polynomial ax^2 + bx + c has a degree of 2.
Here are the default polynomial options and their equivalent custom polynomial entry:
This allows us to create regressions with a custom number of inflection points. An inflection point is a point where the graph changes from concave up to concave down or vice versa. The maximum number of inflection points a polynomial can have is the degree - 2. Having multiple inflection points in our regression allows for having a closer fit minimizing error.
It should be noted that having a closer fit is not inherently better; this can cause overfitting. Overfitting is when a model is too closely fit to the training data and not generalizable to the population data.
panpanXBT BTC Risk MetricThis is the Bitcoin Risk Metric. Inspired by many power law analysts, this script assigns a risk value to the price of Bitcoin. The model uses regression of 'fair value' data to assign risk values and residual analysis to account for diminishing returns as time goes on. This indicator is for long-term investors looking to maximise their returns by highlighting periods of under and overvaluation for Bitcoin.
This is a companion script for panpanXBT BTC Risk Metric Oscillator . Use this indicator in tandem to achieve the view shown in the chart above.
Please note, this indicator will only work on BTCUSD charts but will work on any timeframe.
DISCLAIMER: The product on offer presents a novel way to view the price history of Bitcoin. It should not be relied upon solely to inform financial decisions. What you do with the information is entirely up to you. Please thoroughly consider your decisions and consult many different sources to make sure you're making the most well-informed decision.
### How to Interpret
The risk scale goes from 0 to 100,
Blue - 0 being low risk, and
Red - 100 being high risk.
Low risk values represent periods of historical undervaluation, while high values represent overvaluation. These periods are marked by a colourscale from blue to red.
### Use Cases and Best Practice
A dynamic DCA strategy would work best with this indicator, whereby an amount of capital is deployed/retired on a regular basis. This amount deployed grows or shrinks depending on the proximity of the risk level to the extremes (0 and 100).
Let's say you have a maximum of $500 to deploy per month.
When risk is between 0 and 10, you could deploy the full $500.
When risk is between 10 and 20, you could deploy $400.
When risk is between 20 and 30, you could deploy $300.
When risk is between 30 and 40, you could deploy $200.
When risk is between 40 and 50, you could deploy $100.
Conversely, when risk is above 50, you could:
Sell 1/15th of your BTC stack when risk is between 50 and 60.
Sell 2/15th of your BTC stack when risk is between 60 and 70.
Sell 3/15th of your BTC stack when risk is between 70 and 80.
Sell 4/15th of your BTC stack when risk is between 80 and 90.
Sell 5/15th of your BTC stack when risk is between 90 and 100.
This framework allows the user to accumulate during periods of undervaluation and derisk during periods of overvaluation, capturing returns in the process.
In contrast, simply setting limit orders at 0 and 100 would yield the absolute maximum returns, however there is no guarantee price will reach these levels (see 2018 where the bear market bottomed out at 20 risk, or 2021 where price topped out at 97 risk).
### Caveats
"All models are wrong, some are useful"
No model is perfect. No model can predict exactly what price will do as there are too many factors at play that determine the outcome. We use models as a guide to make better-informed decisions, as opposed to shooting in the dark. This model is not a get rich quick scheme, but rather a tool to help inform decisions should you consider investing. This model serves to highlight price extremities, which could present opportune times to invest.
### Conclusion
This indicator aims to highlight periods of extreme values for Bitcoin, which may provide an edge in the market for long-term investors.
Thank you for your interest in this indicator. If you have any questions, recommendations or feedback, please leave a comment or drop me a message on TV or twitter. I aim to be as transparent as possible with this project, so please seek clarification if you are unsure about anything.
Kaspa Power LawSimple Power Law Indicator for Kaspa with addition of adjustable bands above and below the Power Law Price. Best used on Logarithmic view on Daily Time Frame.
Monte Carlo Shuffled Projection [LuxAlgo]The Monte Carlo Shuffled Projection tool randomly simulates future price points based on historical bar movements made within a user-selected window.
The tool shows potential paths price might take in the future, as well as highlighting potential support/resistance levels.
Note that simulations and their resulting elements are subject to slight changes over time.
🔶 USAGE
By randomly simulating bar movements, a range is developed of potential price action which could be utilized to locate future price development as well as potential support/resistance levels.
Performing a large number of simulations and taking the average at each step will converge toward the result highlighted by the "Average Line", and can point out where the price might develop assuming the trend and amount of volatility persist.
Current closing price + Sum of changes in the calculation window)
This constraint will cause the simulations to always display an endpoint consistent with the current lookback's slope.
While this may be helpful to some traders, this indicator includes an option to produce a less biased range as seen below:
🔶 DETAILS
The Monte Carlo Shuffled Projection tool creates simulations based on the most recent prices within a user-set window. Simulations are done as follows:
Collect each bar's price changes in the user-set window.
Randomize the order of each change in the window.
Project the cumulative sum of the shuffled changes from the current closing price.
Collect data on each point along the way.
This is the process for the Default calculation, for the 'Randomize Direction' calculation, when added onto the front for every other change, the value is inverted, creating the randomized endpoints for each simulation.
The script contains each simulation's data for that bar with a maximum of 1000 simulations.
To get a glimpse behind the scenes each simulation (up to 99) can be viewed using the 'Visualize Simulations' Options as seen below.
Because the script holds the full simulation data, the script can also do calculations on this data, such as calculating standard deviations.
In this script the Standard deviation lines are the average of all standard deviations across the vertical data groups, this provides a singular value that can be displayed a distance away from the simulation center line.
🔶 SETTINGS
Color and Toggle Options are Provided throughout.
Lookback: Sets the number of Bars to include in calculations.
Simulation Count: Sets the number of randomized simulations to calculate. (Max 1000)
Randomize Direction: See Details Above. Creates a more 'Normalized' Distribution
Visualize Simulations: See Details Above. Turns on Visualizations, and colors are randomly generated. Visualized max does not cap the calculated max. If 1000 simulations are used, the data will be from 1000 simulations, however only the last 99 simulations will be visualized.
Standard Deviation Multiplier: Sets the multiplier to use for the Standard Deviation distance away from the center line.
Multiple Non-Linear Regression [ChartPrime]This Pine Script indicator is designed to perform multiple non-linear regression analysis using four independent variables: close, open, high, and low prices. Here's a breakdown of its components and functionalities:
Inputs:
Users can adjust several parameters:
Normalization Data Length: Length of data used for normalization.
Learning Rate: Rate at which the algorithm learns from errors.
Smooth?: Option to smooth the output.
Smooth Length: Length of smoothing if enabled.
Define start coefficients: Initial coefficients for the regression equation.
Data Normalization:
The script normalizes input data to a range between 0 and 1 using the highest and lowest values within a specified length.
Non-linear Regression:
It calculates the regression equation using the input coefficients and normalized data. The equation used is a weighted sum of the independent variables, with coefficients adjusted iteratively using gradient descent to minimize errors.
Error Calculation:
The script computes the error between the actual and predicted values.
Gradient Descent: The coefficients are updated iteratively using gradient descent to minimize the error.
// Compute the predicted values using the non-linear regression function
predictedValues = nonLinearRegression(x_1, x_2, x_3, x_4, b1, b2, b3, b4)
// Compute the error
error = errorModule(initial_val, predictedValues)
// Update the coefficients using gradient descent
b1 := b1 - (learningRate * (error * x_1))
b2 := b2 - (learningRate * (error * x_2))
b3 := b3 - (learningRate * (error * x_3))
b4 := b4 - (learningRate * (error * x_4))
Visualization:
Plotting of normalized input data (close, open, high, low).
The indicator provides visualization of normalized data values (close, open, high, low) in the form of circular markers on the chart, allowing users to easily observe the relative positions of these values in relation to each other and the regression line.
Plotting of the regression line.
Color gradient on the regression line based on its value and bar colors.
Display of normalized input data and predicted value in a table.
Signals for crossovers with a midline (0.5).
Interpretation:
Users can interpret the regression line and its crossovers with the midline (0.5) as signals for potential buy or sell opportunities.
This indicator helps users analyze the relationship between multiple variables and make trading decisions based on the regression analysis. Adjusting the coefficients and parameters can fine-tune the model's performance according to specific market conditions.
Volume-Supported Linear Regression Trend Modified StrategyHi everyone, this will be my first published script on Tradingview, maybe more to come.
For quite some time I have been looking for a script that performs no matter if price goes up or down or sideways. I believe this strategy comes pretty close to that. Although nowhere near the so called "buy&hold equity" of BTC, it has produced consistent profits even when price goes down.
It is a strategy which seems to work best on the 1H timeframe for cryptocurrencies.
Just by testing different settings for SL and TP you can customize it for each pair.
THE STRATEGY:
Basically, I used the Volume Supported Linear Regression Trend Model that LonesomeTheBlue has created and modified a few things such as entry and exit conditions. So all credits go to him!
LONG ENTRY: When there is a bullish cross of the short term trend (the histogram/columns), while the long term trend is above 0 and rising.
SHORT ENTRY: When there is a bearish cross (green to red) of the short-term trend (the histogram/columns), while the long term trend is beneath 0 and decreasing.
LONG EXIT: Bearish crossover of short-term trend while long term trend is below 0
SHORT EXIT: Bullish crossover of short-term trend while long term trend is above 0
Combining this with e.g. a SL of 2% and a TP of 20% (as used in my backtesting), combined with pyramiding and correct risk management, it gives pretty consistent results.
Be aware, this is only for educational purpose and in no means financial advise. Past results do not guarantee future results. This strategy can lose money!
Enjoy :)
PS: It works not only on BTC of course, works even better on some other major crypto pairs. I'll leave it to you to find out which ones ;)
MomentumQ SniperMomentumQ Sniper Indicator
The MomentumQ Sniper is an advanced Tool, designed to provide traders with multi-dimensional market insights. This indicator integrates the Moving Average Reversal Indicator (MARI), Price Countdown logic for reversal detection, and the Nadaraya-Watson Envelope (NWE) for identifying overbought and oversold conditions.
Features:
Moving Average Reversal Indicator (MARI): Calculates and normalizes the distance between the price and a 200-period SMA to signal potential reversals.
Price Countdown: Offers sequence-based analysis to pinpoint potential reversal points through exhaustive price movement tracking.
Nadaraya-Watson Envelope (NWE): Utilizes advanced smoothing to create dynamic envelopes around a central moving line, aiding in the detection of market extremes.
Visualization and Alerts:
Visual Indicators: Plots key elements such as the SMA, MARI distance, and Nadaraya-Watson envelopes, each color-coded to enhance clarity—green for bullish setups and red for bearish setups.
Alert System: Integrates real-time alerts for crucial signals, including bullish and bearish crossovers as identified by MARI and Price Countdown logic, enabling timely and informed trading decisions.
How to Use:
The MomentumQ Sniper is ideal for traders who require detailed and multi-dimensional analysis tools. It effectively combines trend analysis, reversal prediction, and volatility insights into one unified strategy, making it suitable for a variety of market conditions and trading time frames.
Disclaimer:
The MomentumQ Sniper is designed as a supplementary tool for market analysis. It does not guarantee profits and should be used as part of a diversified trading strategy. Past performance is not indicative of future results, and all trading involves risks.
Educational Value:
Beyond aiding in trade execution, this indicator enhances the trader's understanding of market dynamics through advanced technical analysis techniques, providing valuable educational insights into market behavior.
Long-Term Trend DetectorThe Long-Term Trend Detector is a powerful tool designed to identify sustainable trends in price movements, offering significant advantages for traders and investors.
Key Benefits:
1. Projection Confidence: This indicator leverages Pearson's R, a statistical measure that indicates the strength of the linear relationship between price and trend projection. A higher Pearson's R value reflects a stronger correlation, providing increased confidence in the identified trend direction.
2. Adaptive Channel Detection: By calculating deviations and correlations over varying lengths, the indicator dynamically adapts to changing market conditions. This adaptive nature ensures robust trend detection across different time frames.
3. Visual Clarity: The indicator visually displays long-term trend channels on the chart, offering clear insights into potential price trajectories. This visualization aids in decision-making by highlighting periods of strong trend potential.
4. Flexibility and Customization: Users can customize parameters such as deviation multiplier, line styles, transparency levels, and display preferences. This flexibility allows traders to tailor the indicator to their specific trading strategies and preferences.
5. Historical Analysis: The indicator can analyze extensive historical data (up to 5000 bars back) to provide comprehensive trend insights. This historical perspective enables users to assess trends over extended periods, enhancing strategic decision-making.
In summary, the Long-Term Trend Detector empowers traders with accurate trend projections and confidence levels, facilitating informed trading decisions. Its adaptive nature and customizable features make it a valuable tool for identifying and capitalizing on long-term market trends.
TrippleMACDCryptocurrency Scalping Strategy for 1m Timeframe
Introduction:
Welcome to our cutting-edge cryptocurrency scalping strategy tailored specifically for the 1-minute timeframe. By combining three MACD indicators with different parameters and averaging them, along with applying RSI, we've developed a highly effective strategy for maximizing profits in the cryptocurrency market. This strategy is designed for automated trading through our bot, which executes trades using hooks. All trades are calculated for long positions only, ensuring optimal performance in a fast-paced market.
Key Components:
MACD (Moving Average Convergence Divergence):
We've utilized three MACD indicators with varying parameters to capture different aspects of market momentum.
Averaging these MACD indicators helps smooth out noise and provides a more reliable signal for trading decisions.
RSI (Relative Strength Index):
RSI serves as a complementary indicator, providing insights into the strength of bullish trends.
By incorporating RSI, we enhance the accuracy of our entry and exit points, ensuring timely execution of trades.
Strategy Overview:
Long Position Entries:
Initiate long positions when all three MACD indicators signal bullish momentum and the RSI confirms bullish strength.
This combination of indicators increases the probability of successful trades, allowing us to capitalize on uptrends effectively.
Utilizing Linear Regression:
Linear regression is employed to identify consolidation phases in the market.
Recognizing consolidation periods helps us avoid trading during choppy price action, ensuring optimal performance.
Suitability for Grid Trading Bots:
Our strategy is well-suited for grid trading bots due to frequent price fluctuations and opportunities for grid activation.
The strategy's design accounts for price breakthroughs, which are advantageous for grid trading strategies.
Benefits of the Strategy:
Consistent Performance Across Cryptocurrencies:
Through rigorous testing on various cryptocurrency futures contracts, our strategy has demonstrated favorable results across different coins.
Its adaptability makes it a versatile tool for traders seeking consistent profits in the cryptocurrency market.
Integration of Advanced Techniques:
By integrating multiple indicators and employing linear regression, our strategy leverages advanced techniques to enhance trading performance.
This strategic approach ensures a comprehensive analysis of market conditions, leading to well-informed trading decisions.
Conclusion:
Our cryptocurrency scalping strategy offers a sophisticated yet user-friendly approach to trading in the fast-paced environment of the 1-minute timeframe. With its emphasis on automation, accuracy, and adaptability, our strategy empowers traders to navigate the complexities of the cryptocurrency market with confidence. Whether you're a seasoned trader or a novice investor, our strategy provides a reliable framework for achieving consistent profits and maximizing returns on your investment.
Sector ETFs performance overviewThe indicator provides a nuanced view of sector performance through ETF analysis, focusing on long-term price trends and deviations from these trends to gauge relative strength or weakness. It utilizes a methodical approach to smooth out ETF price data and then applies a regression analysis to pinpoint the primary trend direction. By examining how far the current price deviates from this regression line, the indicator identifies potential overbought or oversold conditions within various sectors.
Core Analysis Techniques:
Logarithmic Transformation and Regression: This process transforms ETF closing prices on a logarithmic scale to better understand sector growth patterns and dynamics. A linear regression of these prices helps define the overarching trend, crucial for understanding market movements.
Volatility Bands for Market State Assessment: The indicator calculates standard deviation based on logarithmic prices to establish dynamic bands around the regression line. These bands are instrumental in identifying market states, highlighting when sectors may be overextended from their central trend.
Sector-Specific Analysis: By focusing on distinct sector ETFs, the tool enables targeted analysis across various market segments. This specificity allows for a granular look at sectors like technology, healthcare, and financials, providing insights tailored to each area.
Adaptability and Insight:
Customizable Parameters: The indicator offers users the ability to adjust key parameters such as regression length and smoothing factors. This customization ensures that the analysis can be tailored to individual preferences and market outlooks.
Trend Direction and Momentum: It assesses the ETF's price movement relative to historical data and the established volatility bands, helping to clarify the sector's trend strength and potential directional shifts.
Strategic Application:
Focusing on trend and volatility analysis rather than direct trading signals, the indicator aids in forming a strategic view of sector investments. It's particularly useful for:
Spotting macroeconomic trends through the lens of sector ETF performance.
Informing portfolio decisions with nuanced insights into sector momentum and market conditions.
Anticipating potential market shifts by evaluating how current prices align with historical volatility and trend patterns.
This tool stands out as a vital resource for analyzing sector-level market trends, offering detailed insights into the dynamics of economic sectors for comprehensive market analysis.
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.
SheTrade [Filiwoman]The SheTrade indicator calculates pivot points based on the highest and lowest prices for a certain number of bars. These pivot points are then used to calculate support and resistance levels, which are displayed on the chart as horizontal lines. The indicator also includes an additional function to indicate reference points and support/resistance levels, which makes it easier to identify key levels in the market.
🔶 SETTINGS
To configure the SheTrade indicator in the user menu, I would recommend the following input parameters:
🔹Source: Set the closing price of the asset you want to analyze. This is the default value
🔹ML/TF 10 ST/TF 21: Set the value to 21. This means that the indicator will use the 21 most recent bars to calculate the support and resistance lines.
🔹Max points (3): Set the value to 3. This means that the indicator will consider the support or resistance line to be critical if it has been tested at least 3 times. You can adjust this value depending on your trade.
🔹Min points (1): Set the value to 1. This means that the indicator will consider the support or resistance line as the minimum critical level.
🔹Number of lines: set the value to 2. This means that the indicator will display a maximum of 2 lines on the chart. You can adjust this value depending on your trading strategy.
🔹Line direction: Set the value to "right". This means that the indicator will draw lines to the right of the current one
🔹Line thickness: set the value to 1. This is the default value for line thickness.
🔹Multiplier: Set the value to 2.7. This is the default value for the multiplier used to calculate the upper and lower limits.
🔹ATR: Set the value to 32. This is the default value for the average value.
These settings will provide a clear and concise view of the support and resistance levels of the asset you are analyzing, with highlighted critical levels and regression lines that will help identify trends and potential breakthroughs. You can adjust the parameters as needed according to your trading strategy and timeframe.
One of the unique features of the SheTrade indicator is the use of a core regression algorithm to calculate support and resistance levels. This algorithm takes into account the relative weight of each pivot point, with later pivot points being given more weight. This allows the indicator to adapt to changing market conditions and provide more accurate support and resistance levels.
In addition to the support and resistance levels, the SheTrade indicator also includes a Bollinger band-style envelope that can be used to determine overbought and oversold conditions in the market. This envelope is based on the specified multiplier of the Average True Range indicator (ATR) and can be configured according to user preferences.
In general, the SheTrade indicator is a powerful and flexible tool that can help traders identify key support and resistance levels in the market and make more informed trading decisions. The use of the core regression algorithm and configurable input parameters makes it a versatile and adaptable indicator that can be adapted to the needs of any trader.
Custom Swing Index [AstroHub]Custom Swing Index - Unleashing Precision in Trend Analysis
🌟 Overview:
The Custom Swing Index is a meticulously crafted tool that empowers traders with advanced insights into market dynamics, specifically focusing on identifying potential trend reversals. Developed by AstroHub, this indicator stands out for its unique combination of price-related calculations, ratios, and averages, providing a comprehensive and nuanced view of market sentiment.
📈 Key Components:
Price Calculation:
- Price Change: Captures the difference between the current and previous closing prices.
- High and Low Points: Analyzes the high and low points of each bar for crucial price movement data.
Ratios and Averages:
- Upper-Lower Shadow Ratio: Measures the relationship between the upper and lower shadows.
- Open-Close Ratio: Evaluates the ratio of opening to closing prices.
- Sum Price Changes: Sums up price changes over a specified period.
Differences and Shadows:
- Open-Close Difference: Considers the difference between opening and closing prices.
- Upper and Lower Shadow Ratios: Examines the proportions of upper and lower shadows.
Bar Size Metrics:
- Average Bar Size: Determines the average size of each bar.
- High-Low Difference: Measures the difference between the high and low points.
Swing Indicator Calculation:
- The Custom Swing Index is the result of combining these components, creating a dynamic metric that reflects potential trend reversals.
🚥 How to Use:
Understanding the Indicator:
- Bullish signals may be indicated when the swing index surpasses a defined threshold.
- Bearish signals may be indicated when the swing index falls below the negative threshold.
Visual Interpretation:
- Color-coded bars enhance visual interpretation, turning green for bullish conditions and red for bearish conditions.
Entry Points:
- Look for entry points where circle markings are present, indicating potential opportunities.
Alerts:
- Integrated alerts keep traders informed of significant swings, ensuring timely decision-making.
[S] Rolling TrendlineThe Rolling Linear Regression Trendline is a sophisticated technical analysis tool designed to offer traders a dynamic view of market trends over a selectable period. This indicator employs linear regression to calculate and plot a trendline that best fits the closing prices within a specified window, either defined by a number of bars or a set period in days, independent of the chart's timeframe.
Key Features:
Dynamic Window Selection: Users can choose the calculation window based on a fixed number of bars or days, providing flexibility to adapt to different trading strategies and timeframes. For the 'days' option, the indicator calculates the equivalent number of bars based on the chart's timeframe, ensuring relevance across various market conditions and trading sessions.
Linear Regression Analysis: At its core, the indicator uses linear regression to identify the trend direction by calculating the slope and intercept of the trendline. This method offers a statistical approach to trend analysis, highlighting potential uptrends or downtrends based on the positioning and direction of the trendline.
Customizable Period: Traders can input their desired period (N), allowing for tailored analysis. Whether it's short-term movements or longer-term trends, the indicator can adjust to focus on specific time horizons, enhancing its utility across different trading styles and objectives.
Applications:
Trend Identification: By plotting a trendline that mathematically fits the closing prices over the chosen period, traders can quickly identify the prevailing market trend, aiding in bullish or bearish decision-making.
Support and Resistance: The trendline can also serve as a dynamic level of support or resistance, offering potential entry or exit points based on the price's interaction with the trendline.
Strategic Planning: With the ability to adjust the calculation window, traders can align the indicator with their trading strategy, whether focusing on intraday movements or broader swings.
Using this indicator with other parameters can widen you view of the market and help identifying trends
RPPI Futures & Indices Strategy Tester [SS Premium]Hello everyone,
As promised, here is the strategy companion to the RPPI Futures & Indicies Indicator.
It contains all of the models of the RPPI but the functionality is all about back-testing the strategy. As such, you cannot use this to run probabilities, run autoregression assessments, or do any of the advanced RPPI features, this is solely to allow you to develop and implement a sustainable strategy in your trading using the RPI.
When you launch the indicator, in the settings menu, you will see toggles to customize the strategy you would like to apply:
You can customize your short and long entries and your short and long exits and then review the backtest results of these various combinations.
From there, you can open up tradingview's strategy tester to see the immediate success of the strategy. If you want to test how effective your strategy is further back, you can make use of Tradingview's "Deep Backtesting" option. This allows you to select a start date way in the past, and back-test over numerous months / years, to see if the strategy has been sustainable in the long term.
To read more about the RPPI, you can check out its own page which lists the details of the indicator, how it works and how to use it. As a synopsis, the RPPI is a compendium indicator that contains various models of multiple futures and stocks. This is to attempt to accurately forecast daily, weekly, monthly, 3 month and annual moves on various futures and indices.
This strategy companion will help you hone in on ideal entries and exits and allow you to tailor them to each ticker that you are interested in trading, on whichever timeframe you are interested in trading.
Some important notes when applying the back-testing results:
1. If you are back-testing daily levels, it is recommended to use the 1 to 5-minute chart max.
2. iF you are back-testing weekly levels, it is recommended to use at least 15 to 30 minutes, up to 60 minute candles.
3. Monthly levels, its best to use 1 hour and up.
4. Greater than monthly, its best to use 3 to 4 hours, to daily candles and up.
As always, feel free to leave your questions or suggestions below.
Thank you for reading and, as always, safe trades!
Least Median of Squares Regression | ymxbThe Least Median of Squares (LMedS) is a robust statistical method predominantly used in the context of regression analysis. This technique is designed to fit a model to a dataset in a way that is resistant to outliers. Developed as an alternative to more traditional methods like Ordinary Least Squares (OLS) regression, LMedS is distinguished by its focus on minimizing the median of the squares of the residuals rather than their mean. Residuals are the differences between observed and predicted values.
The key advantage of LMedS is its robustness against outliers. In contrast to methods that minimize the mean squared residuals, the median is less influenced by extreme values, making LMedS more reliable in datasets where outliers are present. This is particularly useful in linear regression, where it identifies the line that minimizes the median of the squared residuals, ensuring that the line is not overly influenced by anomalies.
STATISTICAL PROPERTIES
A critical feature of the LMedS method is its robustness, particularly its resilience to outliers. The method boasts a high breakdown point, which is a measure of an estimator's capacity to handle outliers. In the context of LMedS, this breakdown point is approximately 50%, indicating that it can tolerate corruption of up to half of the input data points without a significant degradation in accuracy. This robustness makes LMedS particularly valuable in real-world data analysis scenarios, where outliers are common and can severely skew the results of less robust methods.
Rousseeuw, Peter J.. “Least Median of Squares Regression.” Journal of the American Statistical Association 79 (1984): 871-880.
The LMedS estimator is also characterized by its equivariance under linear transformations of the response variable. This means that whether you transform the data first and then apply LMedS, or apply LMedS first and then transform the data, the end result remains consistent. However, it's important to note that LMedS is not equivariant under affine transformations of both the predictor and response variables.
ALGORITHM
The algorithm randomly selects pairs of points, calculates the slope (m) and intercept (b) of the line, and then evaluates the median squared deviation (mr2) from this line. The line minimizing this median squared deviation is considered the best fit.
DISCLAIMER
In the LMedS approach, a subset of the data is randomly selected to compute potential models (e.g., lines in linear regression). The method then evaluates these models based on the median of the squared residuals. Since the selection of data points is random, different runs may select different subsets, leading to variability in the computed models.
Universal Forecaster [SS Premium]This is the Universal Forecaster as part of the Elite level.
About:
The universal forecast creates autofitted models for most financial instruments using an ATR approach. It will provide a Bullish and Bearish threshold condition, prospective low targets and prospective high targets. It will autofit and no user inputs are required to manually adjust the parameters.
In addition to this, the indicator also has some build in functions to augment its functionality, including:
a) Built in Autoregression Forecaster;
b) Built in ARIMA plotter;
c) Built in Probability Assessor;
d) Ability to plot next day targets and thresholds;
e) Ability to expand targets up to 3 standard deviations from its projected levels;
d) Has the ability to generate models for most to all timeframes (from as low at 5 minutes to as high as yearly)
Functionality:
Off the bat, the indicator will provide you with the conditional levels and immediate target ranges. A break above a conditional level generally means a move to the high range and a break below, a move to the low range.
If a ticker extends beyond the immediate forecasted range, the indicator has the ability to expand the ranges (see example below):
It will do this automatically in response to a range exceedance.
The indicator anchors from the previous day close, which gives it the ability to show you the next day targets and thresholds:
In addition to being able to plot the next day targets, it is also capable of auto generating a probability assessment based on the model it creates:
The indicator provides 2 probability types, momentum probability which uses technicals and z-score probability which uses standard deviation:
It will display the backtest results as well as a break down of the similar cases identified (see image above).
If there are no cases, the indicator will alert you. You can then change the probability type to see if the other one can find cases:
Make sure when you are running the probability assessment, your chart matches the timeframe you are running the assessment for!
The indicator also provides a trade planner to help you ascertain high probability trades based on each unique ticker's behaviour. When toggled on, it will display the various condition possibilities, and the common resulting behaviour:
You can also get shorter timeframe levels, here is an example of hourly levels:
The indicator also works really well with most Crypto.
Here is BTC using weekly levels:
And ADAUSD using monthly levels:
In addition to running an autoregression forecast, you can also run an ARIMA plot directly from the indicator itself and have it plot the bullish or bearish case:
Bullish case:
Bearish case:
The indicator is intended as a stand-alone indicator and can be used as its own strategy. The strategy is fairly straight forward, a break and hold of the bullish conditional, long to the high targets, inverse for a break of the bearish.
Key take-aways and tips:
Can be used on all timeframes;
When running probabilities, please ensure that you are on the chart you are running the probabilities for. So if you are running for the next day, please make sure you are on the daily timeframe.
The ARIMA and Autoregression will default to whichever timeframe you are on.
And that is the indicator!
Let me know your questions below and enjoy!
