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Computer Science Bachelor in CUHK(SZ)
Quant Researcher with 3+ years of trading experience
Currently managing 5+ trading systems
Specializes in CTA strategy development
Github: https://github.com/CodyOutcast
Quant Researcher with 3+ years of trading experience
Currently managing 5+ trading systems
Specializes in CTA strategy development
Github: https://github.com/CodyOutcast
Zhuo Kai Chen
I personally have some critical thoughts about developing machine learning models as filters for trend-following strategies. We all know that trend-following strategies primarily profit from a few outlier trades that offset most of the losses. This characteristic of profit distribution is difficult to capture with a binary classifier. While we can attempt to minimize this issue by assigning greater weight to the higher profit class, it remains challenging. Intuitively, predicting long-term profits is akin to forecasting prices, which academia often regards as a mystery. Dr. Ernest P. Chan, the author of "Quantitative Trading", stated that using tree models to predict short-term prices is much easier than predicting long-term prices—similar to how forecasting the weather for the next minute is easier than predicting it for tomorrow. I strongly agree and have found success using such models to predict short-term mean reversion strategies.
Recently, a fund manager from Man Group gave a lecture about CTAs (Commodity Trading Advisors) at my university. He mentioned that they rarely use machine learning in their CTA bots, which baffled me. Literally, one of the most successful firms in the world prefers simple rules and intuitive algorithms over sophisticated methods. I asked him why, and he explained:
1. They tried using machine learning to mine alphas but failed miserably.
2. They attempted to use it as a filter, similar to what we discussed in this article, but it barely worked, achieving only 80% correlation. This means it provided almost no additional edge compared to the original strategy.
3. They found success in using machine learning to select the best strategy for a given market.
Regarding the third point, I wondered why they didn’t simply test each strategy for every market and compare the results. However, I assume they find it more efficient to cluster markets for certain strategies, especially since they trade over 6,000 assets. They believe the aforementioned theory explains their obstacles, as they primarily use trend-following strategies for their CTA bots.
Recently, a fund manager from Man Group gave a lecture about CTAs (Commodity Trading Advisors) at my university. He mentioned that they rarely use machine learning in their CTA bots, which baffled me. Literally, one of the most successful firms in the world prefers simple rules and intuitive algorithms over sophisticated methods. I asked him why, and he explained:
1. They tried using machine learning to mine alphas but failed miserably.
2. They attempted to use it as a filter, similar to what we discussed in this article, but it barely worked, achieving only 80% correlation. This means it provided almost no additional edge compared to the original strategy.
3. They found success in using machine learning to select the best strategy for a given market.
Regarding the third point, I wondered why they didn’t simply test each strategy for every market and compare the results. However, I assume they find it more efficient to cluster markets for certain strategies, especially since they trade over 6,000 assets. They believe the aforementioned theory explains their obstacles, as they primarily use trend-following strategies for their CTA bots.
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Zhuo Kai Chen
Ha publicado el artículo Utilización del modelo de aprendizaje automático CatBoost como filtro para estrategias de seguimiento de tendencias 
CatBoost es un potente modelo de aprendizaje automático basado en árboles que se especializa en la toma de decisiones basada en características estacionarias. Otros modelos basados en árboles, como XGBoost y Random Forest, comparten características similares en cuanto a su solidez, capacidad para manejar patrones complejos e interpretabilidad. Estos modelos tienen una amplia gama de usos, desde el análisis de características hasta la gestión de riesgos. En este artículo, vamos a explicar el procedimiento para utilizar un modelo CatBoost entrenado como filtro para una estrategia clásica de seguimiento de tendencias con cruce de medias móviles.
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