Discussing the article: "Self Optimizing Expert Advisors in MQL5 (Part 18): Time Lagged Independent Components Analysis"

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Check out the new article: Self Optimizing Expert Advisors in MQL5 (Part 18): Time Lagged Independent Components Analysis.

We evaluate blind source separation for market noise control using FastICA applied to SMA-filtered, time-lagged OHLC features. The study compares classical and surrogate targets, measures accuracy across lags, tunes KNN models, and inspects residual structure with clustering. Models are exported to ONNX and integrated into an MQL5 Expert Advisor for testing. The result is a reproducible pipeline from data extraction to deployment.

In our previous discussion on building self-optimizing expert advisors, we used the moving average indicator as both the signal source for our trading strategy and the target for our statistical model forecasts.  This article builds beyond on that exploration by considering how moving average indicators can be augmented with tools from statistical processing to suppress noise in market data.  For readers who have not read the previous discussion, a helpful link has been provided here.

Market noise is pervasive and negatively affects our trading strategies. Unfortunately, there are no widely accepted best practices for effectively handling noise. Many solutions have been created over the years by human traders; most notably, a wide array of technical indicators has been developed to alleviate the problem. In this article, we will explore whether these tools may be augmented with statistical signal processing methods. Our proposed solution may allow market technicians to make more effective use of tools they are already familiar with.

In our previous discussion on building self-optimizing expert advisors, we used the moving average indicator as both the signal source for our trading strategy and the target for our statistical model forecasts. This article builds beyond on that exploration by considering how moving average indicators can be augmented with tools from statistical processing to suppress noise in market data. For readers who have not read the previous discussion, a helpful link has been provided here. Market noise is pervasive and negatively affects our trading strategies. Unfortunately, there are no widely accepted best practices for effectively handling noise. Many solutions have been created over the years by human traders; most notably, a wide array of technical indicators has been developed to alleviate the problem. In this article, we will explore whether these tools may be augmented with statistical signal processing methods. Our proposed solution may allow market technicians to make more effective use of tools they are already familiar with. It is challenging for practitioners to identify and isolate sources of noise while preserving the true signal in market data. However, if we assume that noise and signal are independent and are mixed in the observed market data, and that their underlying distributions are stable, it may be possible to isolate them. This is the task solved by blind source separation algorithms. Independent Components Analysis (ICA) is the source separation technique we will explore in this discussion. The ICA algorithm requires the user to specify the number of components that should be identified in the original dataset. The individual components identified by ICA are not explicitly labeled as either noise or true signal. Therefore, we will observe how the accuracy of our model changes as we iteratively increase the number of components to be extracted. Peak performance does not necessarily come from the maximum number of components possible. These unnecessary components could be the channels of noise and other artifacts that were embedded in the original data. We will apply ICA to market data that has already been filtered by technical indicators. Our market data is filtered by simple moving averages (SMA) and lagged by 5 days. When given time-lagged data, ICA could possibly find independent sources of temporal structure in the data that explain the correlation across time. Additionally, our belief is that, by feeding ICA data that has already been filtered by SMA, we may be able to uncover residual sources of noise that our technical indicators could not filter out.

Author: Gamuchirai Zororo Ndawana

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