u/tookietheroookie ·
Reddit — r/algotrading
· April 11, 2026 at 16:40
· ⬆ 17 pts
· 💬 31 comments
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AI Summary
Summary
A novice quant is inquiring about the practical application of Machine Learning in algorithmic trading, specifically asking about model types (sequential/attention), data sources (tick vs. candles), and strategies (mean reversion/momentum) to overcome market noise.
The post is a request for foundational best practices and starting approaches from the community, not a presentation of a developed strategy.
Quality assessment: This is speculative inquiry and noise. It is a beginner's question about methodology with no original research or data-driven thesis.
Score17
Comments31
Upvote %82%
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I was wondering, how ML fits into algorithmic trading? Because of insane amount of noise making it find the edge itself looks like a losing time wasting battle. Are sequential or attention based models any good? Is doing statistical analysis on recent historical data the good way to start. What is the starting approach usually? Do we start with something basic like mean reversion and momentum based approach and try amplify it with ML?
Also whats better? tick level data or different time based candles? I can work around with ML but due to the noise it is just impossible to make any kind of prediction, what are the best practices of keeping the data sane? Thank you for your time.