{ "id": 588404, "title": "A Little Trick for Training with the 'volume' Feature", "url": "/competitions/drw-crypto-market-prediction/discussion/588404", "author_name": "", "post_date": "2025-07-06T06:34:22.387637100Z", "votes": 10, "comment_count": 7, "views": 0, "content": "

Hi everyone,

\n

I'd like to share an idea here. Please bear with me if any part of it seems out of place.

\n

Basic Idea
\nIn cryptocurrency trading, the volume feature can, to some extent, reflect the intensity of trading activity. High volume might signify significant disagreement or a strong consensus among traders, which in turn could affect future price volatility and direction.To add to that, conversely, low volume can also indicate a period of sustained low market sentiment or a \"wait-and-see\" attitude from traders. For instance, the market might have exhibited this characteristic in the short period before New Hampshire passed its bill related to Bitcoin reserves.

\n

Based on this idea, I tried to treat data points with different volume levels differently.

\n

My Method: Binning Based on Volume
\nMy specific steps are as follows:

\n

Quantile Binning: I applied quantile binning to the volume feature for both the training and test sets. In my experiment, I divided the data into 3 bins (low, medium, and high volume).

\n

Independent Training: Next, I trained a separate, independent model for each of these three data subsets. The goal was for each model to specialize in learning the patterns specific to its particular volume range.

\n

Combine & Reorder: After training, I generated predictions for the corresponding test subsets. Finally, I combined the predictions from the three models. The most crucial step here is to reorder the combined predictions according to the original ID column to ensure the submission format is correct.

\n

Model Ensembling for Enhanced Robustness
\nTo potentially make the results more stable, I blended the predictions from the binned models described above with the predictions from a regular model trained on the entire dataset (without binning).

\n

This approach led to a slight improvement in my final submission score.

", "messages": [ { "id": "3242619", "postDate": "07/06/2025 06:34:22", "content": "

Hi everyone,

\n

I'd like to share an idea here. Please bear with me if any part of it seems out of place.

\n

Basic Idea
\nIn cryptocurrency trading, the volume feature can, to some extent, reflect the intensity of trading activity. High volume might signify significant disagreement or a strong consensus among traders, which in turn could affect future price volatility and direction.To add to that, conversely, low volume can also indicate a period of sustained low market sentiment or a \"wait-and-see\" attitude from traders. For instance, the market might have exhibited this characteristic in the short period before New Hampshire passed its bill related to Bitcoin reserves.

\n

Based on this idea, I tried to treat data points with different volume levels differently.

\n

My Method: Binning Based on Volume
\nMy specific steps are as follows:

\n

Quantile Binning: I applied quantile binning to the volume feature for both the training and test sets. In my experiment, I divided the data into 3 bins (low, medium, and high volume).

\n

Independent Training: Next, I trained a separate, independent model for each of these three data subsets. The goal was for each model to specialize in learning the patterns specific to its particular volume range.

\n

Combine & Reorder: After training, I generated predictions for the corresponding test subsets. Finally, I combined the predictions from the three models. The most crucial step here is to reorder the combined predictions according to the original ID column to ensure the submission format is correct.

\n

Model Ensembling for Enhanced Robustness
\nTo potentially make the results more stable, I blended the predictions from the binned models described above with the predictions from a regular model trained on the entire dataset (without binning).

\n

This approach led to a slight improvement in my final submission score.

", "rawMarkdown": "Hi everyone,\n\nI'd like to share an idea here. Please bear with me if any part of it seems out of place.\n\n**Basic Idea**\nIn cryptocurrency trading, the volume feature can, to some extent, reflect the intensity of trading activity. High volume might signify significant disagreement or a strong consensus among traders, which in turn could affect future price volatility and direction.To add to that, conversely, low volume can also indicate a period of sustained low market sentiment or a \"wait-and-see\" attitude from traders. For instance, the market might have exhibited this characteristic in the short period before New Hampshire passed its bill related to Bitcoin reserves.\n\nBased on this idea, I tried to treat data points with different volume levels differently.\n\n**My Method**: Binning Based on Volume\nMy specific steps are as follows:\n\n**Quantile Binning**: I applied quantile binning to the volume feature for both the training and test sets. In my experiment, I divided the data into 3 bins (low, medium, and high volume).\n\nIndependent Training: Next, I trained a separate, independent model for each of these three data subsets. The goal was for each model to specialize in learning the patterns specific to its particular volume range.\n\nCombine & Reorder: After training, I generated predictions for the corresponding test subsets. Finally, I combined the predictions from the three models. The most crucial step here is to reorder the combined predictions according to the original ID column to ensure the submission format is correct.\n\n**Model Ensembling for Enhanced Robustness**\nTo potentially make the results more stable, I blended the predictions from the binned models described above with the predictions from a regular model trained on the entire dataset (without binning).\n\nThis approach led to a slight improvement in my final submission score.", "votes": null }, { "id": "3242728", "postDate": "07/06/2025 09:42:31", "content": "

Thanks for sharing the detailed methodology！Volume-based binning and ensembling is a clever way to handle different market regimes.

", "rawMarkdown": "Thanks for sharing the detailed methodology！Volume-based binning and ensembling is a clever way to handle different market regimes.", "votes": null }, { "id": "3242977", "postDate": "07/06/2025 15:58:09", "content": "

Thanks to share with us . Interesting! @visterbai

", "rawMarkdown": "Thanks to share with us . Interesting! @visterbai", "votes": null }, { "id": "3242995", "postDate": "07/06/2025 16:18:57", "content": "

Glad to see someone else experimenting with binning!

", "rawMarkdown": "Glad to see someone else experimenting with binning!", "votes": null }, { "id": "3243213", "postDate": "07/06/2025 21:47:09", "content": "

Nicely done! @visterbai

", "rawMarkdown": "Nicely done! @visterbai", "votes": null }, { "id": "3243750", "postDate": "07/07/2025 13:47:24", "content": "

xie le lao di ☺️

", "rawMarkdown": "xie le lao di ☺️", "votes": null }, { "id": "3243770", "postDate": "07/07/2025 14:07:53", "content": "

Very useful, thanks.

", "rawMarkdown": "## Very useful, thanks.", "votes": null }, { "id": "3243963", "postDate": "07/07/2025 17:13:36", "content": "

Thanks to share with us . Interesting and useful！

", "rawMarkdown": "Thanks to share with us . Interesting and useful！", "votes": null } ], "comments": [ { "id": 3242728, "author_name": "z1493916656", "author_url": "", "post_date": "07/06/2025 09:42:31", "content": "

Thanks for sharing the detailed methodology！Volume-based binning and ensembling is a clever way to handle different market regimes.

", "votes": null, "replies": [] }, { "id": 3242977, "author_name": "sadiashahidlatif", "author_url": "", "post_date": "07/06/2025 15:58:09", "content": "

Thanks to share with us . Interesting! @visterbai

", "votes": null, "replies": [] }, { "id": 3242995, "author_name": "taylorsamarel", "author_url": "", "post_date": "07/06/2025 16:18:57", "content": "

Glad to see someone else experimenting with binning!

", "votes": null, "replies": [] }, { "id": 3243213, "author_name": "hijabzahra8", "author_url": "", "post_date": "07/06/2025 21:47:09", "content": "

Nicely done! @visterbai

", "votes": null, "replies": [] }, { "id": 3243750, "author_name": "shinchen93", "author_url": "", "post_date": "07/07/2025 13:47:24", "content": "

xie le lao di ☺️

", "votes": null, "replies": [] }, { "id": 3243770, "author_name": "", "author_url": "", "post_date": "07/07/2025 14:07:53", "content": "

Very useful, thanks.

", "votes": null, "replies": [] }, { "id": 3243963, "author_name": "liujianingcfec", "author_url": "", "post_date": "07/07/2025 17:13:36", "content": "

Thanks to share with us . Interesting and useful！

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "3242619": "Hi everyone,\n\nI'd like to share an idea here. Please bear with me if any part of it seems out of place.\n\n**Basic Idea**\nIn cryptocurrency trading, the volume feature can, to some extent, reflect the intensity of trading activity. High volume might signify significant disagreement or a strong consensus among traders, which in turn could affect future price volatility and direction.To add to that, conversely, low volume can also indicate a period of sustained low market sentiment or a \"wait-and-see\" attitude from traders. For instance, the market might have exhibited this characteristic in the short period before New Hampshire passed its bill related to Bitcoin reserves.\n\nBased on this idea, I tried to treat data points with different volume levels differently.\n\n**My Method**: Binning Based on Volume\nMy specific steps are as follows:\n\n**Quantile Binning**: I applied quantile binning to the volume feature for both the training and test sets. In my experiment, I divided the data into 3 bins (low, medium, and high volume).\n\nIndependent Training: Next, I trained a separate, independent model for each of these three data subsets. The goal was for each model to specialize in learning the patterns specific to its particular volume range.\n\nCombine & Reorder: After training, I generated predictions for the corresponding test subsets. Finally, I combined the predictions from the three models. The most crucial step here is to reorder the combined predictions according to the original ID column to ensure the submission format is correct.\n\n**Model Ensembling for Enhanced Robustness**\nTo potentially make the results more stable, I blended the predictions from the binned models described above with the predictions from a regular model trained on the entire dataset (without binning).\n\nThis approach led to a slight improvement in my final submission score.", "3242728": "Thanks for sharing the detailed methodology！Volume-based binning and ensembling is a clever way to handle different market regimes.", "3242977": "Thanks to share with us . Interesting! @visterbai", "3242995": "Glad to see someone else experimenting with binning!", "3243213": "Nicely done! @visterbai", "3243750": "xie le lao di ☺️", "3243770": "## Very useful, thanks.", "3243963": "Thanks to share with us . Interesting and useful！" }, "source": "meta" }