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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
パブリッシュされた記事CatBoost機械学習モデルをトレンド追従戦略のフィルターとして活用する 
CatBoostは、定常的な特徴量に基づいて意思決定をおこなうことに特化した、強力なツリーベースの機械学習モデルです。XGBoostやRandom Forestといった他のツリーベースモデルも、堅牢性、複雑なパターンへの対応力、そして高い解釈性といった点で共通した特長を備えています。これらのモデルは、特徴量分析からリスク管理に至るまで、幅広い分野で活用されています。本記事では、学習済みのCatBoostモデルを、従来型の移動平均クロスを用いたトレンドフォロー戦略のフィルターとして活用する手順を解説します。
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