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The definition of a Multi-Currency Expert Advisor in this article is one Expert Advisor or trading robot that can trade (open orders, close orders and manage orders for example: Trailing Stop Loss and Trailing Profit) for more than one symbol pair from only one symbol chart, where in this article Expert Advisor will trade for 30 pairs.
This time we will use only 1 indicator, namely Triangular moving average in multi-timeframes or single timeframe mode.
In this article, Expert Advisor

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Tags: -1 or 3*2>(0+5+774-774), -1 or 3*2<(0+5+774-774), -1 or 3+774-774-1=0+0+0+1, 0))xor"z, 0))xor'z, 17*if(now()=sysdate(), 177pvusdyc, 17wff7ylgd, sleep(15), sleep(6)

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MQL5 Freelance is a specialized service for trading application developers. Traders come here when they need custom-made trading robots, indicators, and other utility apps developed in MQL5/MQL4, Python, C++, and other modern programming languages.

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Tags: -1 or 2+169-169-1=0+0+0+1, -1 or 3*2>(0+5+169-169), -1 or 3*2<(0+5+169-169), -1 or 3+169-169-1=0+0+0+1, 0))xor"z, 0))xor'z, 15), 17*if(now()=sysdate(), 17h1yonimi, sleep(15)

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Several fellow traders sent emails or commented about how to use this Multi-Currency EA on brokers with symbol names that have prefixes and/or suffixes, and also how to implement trading time zones or trading time sessions on this Multi-Currency EA.

Therefore, on this occasion I will explain and create a function to be implemented in the EA in the previous article FXSAR_MTF_MCEA by adding an automatic detection function for broker symbols with prefixes and/or suffixes and

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Object detection in video has a number of certain characteristics and must solve the problem of changes in object features caused by motion, which are not encountered in the image domain. One of the solutions is to use temporal information and combine features from adjacent frames. The paper "FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors" proposes a new approach to detecting objects in video. The authors of the article improve the quality of queries for Transformer-based

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Principal Component Analysis (PCA) is the focusing on only the ‘principal components’ among the many dimensions of a data set, such that one is reducing the dimensions of that data set by ignoring the ‘non-principal’ parts.

PCA though, with eigen values & vectors, take on a slightly deeper approach. Typically, data sets that are handled under PCA are in a matrix format and the principal components, that are sought from a matrix would be a single vector column (or row) that

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