Discussing the article: "Multiple Symbol Analysis With Python And MQL5 (Part II): Principal Components Analysis For Portfolio Optimization"
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Check out the new article: Multiple Symbol Analysis With Python And MQL5 (Part II): Principal Components Analysis For Portfolio Optimization.
Managing trading account risk is a challenge for all traders. How can we develop trading applications that dynamically learn high, medium, and low-risk modes for various symbols in MetaTrader 5? By using PCA, we gain better control over portfolio variance. I’ll demonstrate how to create applications that learn these three risk modes from market data fetched from MetaTrader 5.
I believe a visual demonstration of the algorithm at work would be of much benefit to readers that may be encountering the algorithm for the first time. I’ll take the picture of the MQL5 logo in Fig 1, and first convert it to black and white. This black and white filter will make it easier for us to apply and visually see what the PCA algorithm is doing to our data.
Author: Gamuchirai Zororo Ndawana