If someone were to come up with an idea for an investment, they would try to test it with historical data or simulation. If this verification shows a promising return, they would implement the idea, likely as an automatic or a semi-automatic trading system, and then put real money into it. But the story does not end here. Because it is built on assumptions and heuristics, everyone knows any investment strategy will have an expiry date; we just never know it beforehand. Thus, the user of a trading algorithm will have to continuously monitor its performance. When it no longer meets the expectations, it will need some tune-ups, or may even face retirement.
The same should happen to machine learning components in a larger system.
هذه القصة مأخوذة من طبعة September 2021 من DataQuest.
ابدأ النسخة التجريبية المجانية من Magzter GOLD لمدة 7 أيام للوصول إلى آلاف القصص المتميزة المنسقة وأكثر من 9,000 مجلة وصحيفة.
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هذه القصة مأخوذة من طبعة September 2021 من DataQuest.
ابدأ النسخة التجريبية المجانية من Magzter GOLD لمدة 7 أيام للوصول إلى آلاف القصص المتميزة المنسقة وأكثر من 9,000 مجلة وصحيفة.
بالفعل مشترك? تسجيل الدخول
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