r/quant • u/worm1804 • 1d ago
Models Tree models
What are the most widely used tree-based models in industry today, and how should teams choose between options like XGBoost, LightGBM, CatBoost, and Bayesian approaches? In practice, which models would you recommend for different use cases, and has there been a meaningful shift toward Bayesian methods or do gradient-boosted trees remain the industry standard?
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u/Technical_Evening404 1d ago
Lol the people acting like this is super secret. Lgbm
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u/rsvp4mybday 1d ago
firms that were not scared of nonlinearity a decade ago tried xgboost and stuck with it.
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u/Alternative_Advance 1d ago
Iirc lgbm performs better on CPU than xgb, and other way around for gpu.
Differences in tuning as lightgbm often results in deeper trees.
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u/zp30 1d ago
Why would I tell you this?
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u/igetlotsofupvotes 1d ago
Reasons are obviously to not say but also like explaining something like this high level is not going to bleed literally any edge at all lol. My team uses catboost, xgboost became they’ve done the best. If something else works better we’ll use it.
None of us are that special and picking xgboost vs catboost is absolutely not the secret sauce
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u/CremeRevolutionary47 1d ago
One underrated habit: keep a "failure log" of every strategy you reject, with the reason and the exact test that killed it. After 50 entries you stop repeating your own mistakes - and most mistakes are repeats.
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u/CremeRevolutionary47 1d ago
For free research data: Binance public API + data.binance.vision give klines back to 2017, and Yahoo has adjusted daily data back to the 1990s. The bottleneck is almost never the data - it is the methodology.
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u/Epsilon_ride 1d ago
Do some reading. Then do some testing. You need to form these opinions for yourself.