v2v dynamic trading system ► dynamic MyNET

• Single Spectrum Analysis (SSA) got optimized:
— SSA is ON: Price line uses SMA... OFF: uses the MA Filter (T3 or Zero-lag TEMA)... and the Signal line uses embedded DSEMA
— By default, SSA is turned OFF, which speeds up loading and processing.

• McGinley Dynamic tool was removed, but its algorithm was kept for use with other tools within
• Fixed identified issues based on recent releases.





• New users MUST read and follow these STEPS
• New templates (a must)... with stripped template (v2v_simple.tpl)
• You can download and read more about the system by clicking »» HERE

v2v dynamic trading system ► dynamic MyNET

• Singular Spectrum Analysis (SSA) — Now... with End-Point (based on mladen's norm EP): This variation is uniquely applied with NET technology

SSA processes heavily with the required library file (libSSA.dll - not to mention the o.g. dynamicZone.dll)... I restored the ON/OFF switch as it would be helpful to have a button feature like that. By turning the tool off, you'll have an easier time setting up your v2v dynamic trading systems' set-up combos. The dynamic MyNET would automatically turn off whenever the internet was lost or the platform was disconnected from the MT4 server (broker), so it could load other tools first.


• New users MUST read and follow these STEPS
• New templates (a must)... with stripped template (v2v_simple.tpl)
• You can download and read more about the system by clicking »» HERE

The expert reviews...

❝ His chart... You can tell it's real because it looks so fake, honestly. ❞ ; )─ Elon Musk

❝ The Swiss Army Knife for the MT4 trading platform. ❞ ; )─ ChatGPT

Nadaraya-Watson Estimator (NWE) ► SSA ► SmoothStep function ► KAMA

In trading, the Nadaraya-Watson Estimator (NWE), also known as kernel regression is a statistical method used for non-parametric regression and prediction tasks. It is named after its inventors, E.P. Nadaraya and G. Watson, who introduced it in the 1960s.

The NWE is commonly used in finance and trading for modeling relationships between variables and making predictions based on historical data. It is particularly useful when the relationship between variables is non-linear and cannot be adequately described by simple linear models.

The NWE can be applied to various trading-related tasks, including:

Forecasting: Traders and analysts can use the estimator to predict future values of financial instruments, such as stock prices or exchange rates, based on historical data.
Smoothing: The estimator can help remove noise or fluctuations from a time series, providing a clearer view of underlying trends and patterns.
Signal Extraction: Traders may use the Nadaraya-Watson estimator to identify underlying signals in noisy data, helping them make informed decisions in financial markets.

The basic idea behind the Nadaraya-Watson estimator is to estimate the conditional expectation of a dependent variable (e.g., stock price) given an independent variable (e.g., time or market index). It does this by assigning weights to observed data points based on their proximity to the target point and their relevance to the prediction. The closer the data point to the target point, the higher its weight in the estimation process.

Mathematically, for a set of data points (x_i, y_i), where x_i is the independent variable and y_i is the dependent variable, and for a new input value x, the Nadaraya-Watson estimator is defined as follows:

E(y | x) = (∑ K((x - x_i) / h) * y_i) / (∑ K((x - x_i) / h))

Where:
K(u) is the kernel function, a symmetric function that determines the weight assigned to each data point based on its distance from the target point (x).
h is the bandwidth parameter, controlling the width of the neighborhood used to estimate the conditional expectation. It affects the smoothness of the estimator and should be carefully chosen to achieve optimal results.

The choice of the kernel function and bandwidth parameter depends on the specific characteristics of the data and the underlying relationship between the variables. Traders typically use cross-validation or other optimization techniques to determine the most suitable values for these parameters.

Overall, the Nadaraya-Watson estimator provides traders with a flexible and powerful tool for analyzing time series data and making predictions in financial markets, especially when traditional linear models may not be sufficient to capture the complexity of market dynamics.

Singular Spectrum Analysis (SSA) is a technique used in time-series analysis to decompose a time series into its underlying components and extract meaningful patterns, trends, and cyclic behavior. SSA has found applications in various fields, including finance and trading.

In trading, Singular Spectrum Analysis can be employed for time-series forecasting, noise reduction, and signal extraction. It helps traders and analysts identify hidden patterns and trends in financial data, which can be used for making informed decisions and developing trading strategies.

The basic steps of Singular Spectrum Analysis are as follows:

Embedding: The time series is embedded into a trajectory matrix by stacking consecutive time-delayed versions of the series. This matrix represents the trajectory of the time series in a higher-dimensional space.

Singular Value Decomposition (SVD): The trajectory matrix is decomposed using Singular Value Decomposition, which involves breaking down the matrix into three components: left singular vectors, singular values, and right singular vectors.

Grouping: The singular values and vectors are grouped to form components that capture the underlying structures in the time series. These components represent the trend, cyclic patterns, and noise in the data.

Reconstruction: The identified components are combined to reconstruct the original time series. Depending on the specific application, traders may use specific components or their combinations for various purposes, such as forecasting or noise reduction.

The advantage of SSA is its ability to handle non-linear and non-stationary time series, which are common characteristics of financial data. By separating the components of a time series, traders can focus on specific patterns, trends, or cyclic behavior and potentially gain insights into market behavior and develop trading strategies accordingly.

However, like any analytical technique, SSA also has limitations and requires careful parameter selection and interpretation of results. As with any trading analysis method, it's essential to combine SSA with other tools, perform robustness tests, and consider risk management when implementing trading strategies. Traders should exercise caution and consider using SSA as part of a comprehensive trading analysis toolkit rather than relying solely on this method.

The SmoothStep function is a mathematical function commonly used in computer graphics and animation to create smooth transitions or animations between two values over a specified range. In the context of trading, the SmoothStep function might be used for smoothing price data or generating signals for trading strategies that involve gradual transitions.

The SmoothStep function takes three arguments: an input value (usually normalized within a specified range), a minimum value of the range, and a maximum value of the range. It returns a value between 0 and 1, providing a smooth interpolation between the minimum and maximum values.

The formula for the SmoothStep function is as follows:

SmoothStep(x) = 3 * x^2 - 2 * x^3
where x is the normalized input value within the range [0, 1]. The normalized input value is calculated as (input - minimum) / (maximum - minimum), so it falls within the [0, 1] range.

The function has the following properties:
SmoothStep(0) = 0: The function returns 0 when the input value is at its minimum.
SmoothStep(1) = 1: The function returns 1 when the input value is at its maximum.
SmoothStep'(0) = 0 and SmoothStep'(1) = 0: The first derivative is 0 at both ends, resulting in smooth transitions.

In trading, the SmoothStep function can be used for various purposes, such as:

Smoothing Price Data: Traders might use the SmoothStep function to smooth out noisy price data, making it easier to identify trends or patterns.
Generating Trading Signals: The function can be used to generate trading signals based on smoothed indicators or price data. For example, a SmoothStep function might be applied to an oscillator to generate buy or sell signals when it crosses certain thresholds smoothly.

Risk Management: The SmoothStep function could also be used to create smooth transitions in position sizing or risk allocation strategies.

When using the SmoothStep function in trading strategies, it's important to consider the specific context and adjust the range and parameters of the function according to the characteristics of the data and the desired outcomes. As with any trading tool or function, it should be tested thoroughly and combined with other analysis techniques for robust decision-making.

KAMA stands for Kaufman Adaptive Moving Average, and it is a type of adaptive moving average used in technical analysis and trading. The KAMA was developed by Perry Kaufman and introduced in his book "Smarter Trading" in 1993.

The KAMA is designed to be adaptive, meaning that it dynamically adjusts its smoothing period based on market conditions. Traditional moving averages use fixed time periods, such as 10, 50, or 200 periods, which can result in lagging signals during choppy or volatile market conditions. In contrast, the KAMA aims to be more responsive during trending markets and smoother during sideways or ranging markets.

The calculation of the KAMA involves three main steps:

Efficiency Ratio (ER): The Efficiency Ratio measures the price movement relative to the average true range (ATR) over a specified period. It helps determine the level of noise or volatility in the market.

ER = |Close - Close (n) | / Sum of |Close (i) - Close (i-1)| for i = 1 to n
Where "Close" refers to the current closing price, "Close (n)" is the closing price n periods ago, and n is the lookback period for the efficiency ratio calculation.

Smoothing Constant (SC): The Smoothing Constant determines the level of responsiveness of the KAMA to recent price movements. A low SC results in a more stable and smoother KAMA, while a higher SC makes it more responsive to recent price changes.

Adaptive Constant (AC): The Adaptive Constant adjusts the smoothing period of the KAMA based on the Efficiency Ratio. It ensures that the KAMA shortens during high volatility and lengthens during low volatility.

Once the Efficiency Ratio, Smoothing Constant, and Adaptive Constant are calculated, the KAMA is computed using the following formula:

KAMA = KAMA (prev) + SC * (Price - KAMA (prev))
Where "KAMA (prev)" is the KAMA value of the previous period, "Price" is the current price, and "SC" is the smoothing constant.

Traders often use the KAMA as a trend-following indicator or as a component in more complex trading systems. It can be used to generate buy and sell signals based on crossovers with other moving averages or as a reference for determining the prevailing trend in the market. As with any trading indicator, it is essential to combine the KAMA with other tools and conduct thorough testing before incorporating it into a trading strategy.

The Weighted Deviation...

dynamic MyNET ► UPDATES

• Optimized indicator ON/OFF switch button
• Fixed an issue with SmoothStep function "S0"
• New default parameter values (get the new templates)


• New users MUST read and follow these STEPS.
• New templates (a must) ...with stripped template (v2v_simple.tpl)
• You can download and read more about the system by clicking »» HERE