Equilibrium Zones for Optimal Game Start
This indicator is a fully functional prototype that is part of a plan to create an effective optimal trading strategy based on Nash game theory.
Combining various mathematical and statistical methods, this indicator provides traders with an efficient tool for analyzing and forecasting market dynamics. It utilizes adaptive approaches to calculate periods and zone boundaries, and applies Gaussian kernel for smoother data visualization. In this description, we will thoroughly explain each step of the calculation process and demonstrate the impact of each parameter on the indicator's results.
Algorithm Description:
The Adaptive Trading Zones indicator is a tool for market analysis and determining optimal trading zones to make efficient and effective decisions regarding opening, closing, increasing, or decreasing trading positions.
It creates independent forecasted buying and selling zones, each calculated as a separate channel using standard deviation, Gaussian kernel, and super-exponential smoothing.
As a decision support system, the indicator utilizes the RSI (Relative Strength Index) and Stochastic RSI indicators implemented as candle monitors on different timeframes. Users can simultaneously monitor indicator changes on four different timeframes.
Additional decision-support functionality includes dynamic calculation of support and resistance levels. Users can choose between two sets of proposed levels, Fibonacci levels or My Levels, to quickly identify significant levels on the chart.
In the next version of the indicator, all decision-support elements will form the basis of the functionality for signal diversification and automated position size calculation. Essentially, they will become signal filters, along with the application of additional statistical functions to manage optimal position sizing and probability calculations for trend continuation or reversal. This will enable the full implementation of an optimal counter-trend strategy based on Nash's game theory principles.
Combining various mathematical and statistical methods, this indicator provides traders with an efficient tool for analyzing and forecasting market dynamics. It utilizes adaptive approaches to calculate periods and zone boundaries, and applies Gaussian kernel for smoother data visualization. In this description, we will thoroughly explain each step of the calculation process and demonstrate the impact of each parameter on the indicator's results.
Algorithm Description:
- Variance: The calculated variance value is used for more accurate market forecasting and adjusting the forecast zones. This allows the indicator to be more flexible and adaptive to price changes in the market. By considering the variance value, the indicator can adjust the boundaries of the trading zones to reflect the current volatility and market conditions. This helps traders make more informed trading decisions and adapt to the current market situation. Please read the detailed explanation of the corresponding variables in the indicator settings.
- Adaptive Calculation Period: Instead of using a fixed calculation period length, the indicator dynamically calculates the period for each subsequent bar based on price changes and other factors. This enables the indicator to be more flexible and adaptive to the current market dynamics. At any stage of the calculation, users can disable the period adaptation feature and enter their own fixed values. Please read the detailed explanation of the corresponding variables in the indicator settings.
- Standard Deviation: The calculation of standard deviation utilizes exponential smoothing and adaptive exponent. This allows for a more flexible adaptation of the standard deviation value to changing market conditions and price variabilities, considering the influence of recent data on the final result. It eliminates the need for traditional multipliers or shifts that traders usually use to adjust the standard deviation value when using built-in functions. This can be particularly useful in rapidly changing volatilities or continuously shifting trends. Please read the detailed explanation of the corresponding variables in the indicator settings.
- Super Exponential Smoothing: This smoothing method adapts the period length to changing market conditions, enhancing the accuracy and efficiency of signals. Users have the option to apply or disable this function at any stage of the calculation in the indicator settings. Please read the detailed explanation of the corresponding variables in the indicator settings.
- Gaussian Kernel and Weighted Evaluation: In the final data smoothing before visualizing the trading zone charts, the Nadaraya-Watson estimator-based method is employed, using Gaussian kernel as the weight function. Applying the Gaussian kernel allows for smoother data and more reliable boundary calculations for decision-making. The calculation function for the Gaussian kernel also includes an adaptively adjustable power exponent. Please read the detailed explanation of the corresponding variables in the indicator settings.
The Adaptive Trading Zones indicator is a tool for market analysis and determining optimal trading zones to make efficient and effective decisions regarding opening, closing, increasing, or decreasing trading positions.
It creates independent forecasted buying and selling zones, each calculated as a separate channel using standard deviation, Gaussian kernel, and super-exponential smoothing.
As a decision support system, the indicator utilizes the RSI (Relative Strength Index) and Stochastic RSI indicators implemented as candle monitors on different timeframes. Users can simultaneously monitor indicator changes on four different timeframes.
Additional decision-support functionality includes dynamic calculation of support and resistance levels. Users can choose between two sets of proposed levels, Fibonacci levels or My Levels, to quickly identify significant levels on the chart.
In the next version of the indicator, all decision-support elements will form the basis of the functionality for signal diversification and automated position size calculation. Essentially, they will become signal filters, along with the application of additional statistical functions to manage optimal position sizing and probability calculations for trend continuation or reversal. This will enable the full implementation of an optimal counter-trend strategy based on Nash's game theory principles.
הערות שחרור:
Added middle channel.
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Change range of adaptive period value
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Change type of Gaussian Kernel
הערות שחרור:
Change calculate Middle Zone
הערות שחרור:
Added additional zone boundaries and a middle line
הערות שחרור:
Changes made to the calculation of 'My Levels'
הערות שחרור:
Fixed errors in settings
Added choice of nuclear smoothing type for periods with low volatility
Added choice of nuclear smoothing type for periods with low volatility
הערות שחרור:
Add NWE
הערות שחרור:
- New Support / Resistance Levels, calculated using the Sum of Absolute Error (SAE) values
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- Added selection of Gaussian Kernel calculation
הערות שחרור:
Fixed settings
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Increase Range Period and other settings
Min - 120
Recomended - 550
Min - 120
Recomended - 550
הערות שחרור:
Change settings
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Change settings
הערות שחרור:
Fixed errors
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Fixed settings
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Removed service settings that were necessary for the first stage of testing
הערות שחרור:
Added independent levels
הערות שחרור:
Fixed bugs
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Fixed settings
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Added area around levels
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Statistically Significant Price Equilibrium Areas
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Bug’s fixed
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Added TrendLine Signals
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Added TrendLine Signals
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Bugs fixed
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Bugs fixed
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Bugs fixed
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Change calculate levels
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Fixed signals
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Added probability for signals
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Added Signals Filtred
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Fixed Settings
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Added probability Prototype
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Bugs fixed
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Bugs fixed
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Change setting Levels
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Bugs fixed
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Bugs fixed
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Bugs fixed
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Added Stochastic Signals
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Bugs fixed
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Bugs Fixed
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New signal filters
הערות שחרור:
Bugs fixed
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Bugs fixing
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Bags fixed
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Bugs fixed
הערות שחרור:
Add Channel
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Add levels
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Bugs fixed
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Bugs fixed
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Bugs fixed
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Bugs fixed
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Bugs fixed
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Add Mote-Carlo Cloud
הערות שחרור:
Bugs fixed