Discussing the article: "Codex Pipelines: From Python to MQL5 for Indicator Selection — A Multi-Quarter Analysis of the FXI ETF"
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Check out the new article: Codex Pipelines: From Python to MQL5 for Indicator Selection — A Multi-Quarter Analysis of the FXI ETF.
We continue our look at how MetaTrader can be used outside its forex trading ‘comfort-zone’ by looking at another tradable asset in the form of the FXI ETF. Unlike in the last article where we tried to do ‘too-much’ by delving into not just indicator selection, but also considering indicator pattern combinations, for this article we will swim slightly upstream by focusing more on indicator selection. Our end product for this is intended as a form of pipeline that can help recommend indicators for various assets, provided we have a reasonable amount of their price history.
In the last article, we looked at how various preset indicator pairs can be analyzed and sifted for what works best, when we need to trade the VGT ETF. Our focus then was coming up with two complementary indicators that present an assortment of signal patterns so that they can be put to work as soon as possible. In that article, we did not shed much light on how the pool of 5 pairs of complementary indicators were selected, to begin with. For this article, therefore, we seek to tackle this area.
The choosing of technical indicators, in any trading system, can often be approached without enough methodical discipline, which can result in the end-use of indicators being determined by preference, anecdote, or biased historical interpretations. Such s none structured, almost whimsical approach can introduce survivorship-bias, hindsight-confirmation, structural-overfitting and the overall undermining of analytical integrity. A diligent process is always preferred in order to generate reliable signals across different market regimes.
The case for having such a system is even more poignant when faced with an ETF such as the FXI. Its quarterly behavior reflects shifting volatility conditions with changing momentum structures as well as fluctuations in liquidity. Indicators that show promise in what market regime, can easily falter in another. We therefore seek to remedy this by proposing a rigorous Python-based sequence of steps that culminate in use within a wizard assembled Expert Advisor.
Author: Stephen Njuki