{
  "id": 540421,
  "title": "Reference to last Jane Street competition",
  "url": "/competitions/jane-street-real-time-market-data-forecasting/discussion/540421",
  "author_name": "",
  "post_date": "2024-10-14T14:18:51.699893100Z",
  "votes": 12,
  "comment_count": 4,
  "views": 0,
  "content": "<p>As a student interested in quant finance and tabular data, this competition is a dream come true! My thanks to Jane Street and Kaggle!</p>\n<p>The previous competition was the <a href=\"https://www.kaggle.com/competitions/jane-street-market-prediction\" target=\"_blank\">Jane Street Market Prediction</a>, with the 3 best available solutions being:</p>\n<ul>\n<li><a href=\"https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224348\" target=\"_blank\"><em>1st Place</em></a></li>\n<li><a href=\"https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224713\" target=\"_blank\"><em>3rd Place</em></a></li>\n<li><a href=\"https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/226837\" target=\"_blank\"><em>10th Place</em></a></li>\n</ul>\n<p>Best of luck to all participants!</p>",
  "messages": [
    {
      "id": "3017105",
      "postDate": "10/14/2024 14:18:51",
      "content": "<p>As a student interested in quant finance and tabular data, this competition is a dream come true! My thanks to Jane Street and Kaggle!</p>\n<p>The previous competition was the <a href=\"https://www.kaggle.com/competitions/jane-street-market-prediction\" target=\"_blank\">Jane Street Market Prediction</a>, with the 3 best available solutions being:</p>\n<ul>\n<li><a href=\"https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224348\" target=\"_blank\"><em>1st Place</em></a></li>\n<li><a href=\"https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224713\" target=\"_blank\"><em>3rd Place</em></a></li>\n<li><a href=\"https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/226837\" target=\"_blank\"><em>10th Place</em></a></li>\n</ul>\n<p>Best of luck to all participants!</p>",
      "rawMarkdown": "As a student interested in quant finance and tabular data, this competition is a dream come true! My thanks to Jane Street and Kaggle!\n\nThe previous competition was the [Jane Street Market Prediction](https://www.kaggle.com/competitions/jane-street-market-prediction), with the 3 best available solutions being:\n\n- [*1st Place*](https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224348)\n- [*3rd Place*](https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224713)\n- [*10th Place*](https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/226837)\n\nBest of luck to all participants!",
      "votes": null
    },
    {
      "id": "3017140",
      "postDate": "10/14/2024 14:44:58",
      "content": "<p>Nice to meet you here. I saw your notebook in the UM competition and think it was done very well.</p>",
      "rawMarkdown": "Nice to meet you here. I saw your notebook in the UM competition and think it was done very well.",
      "votes": null
    },
    {
      "id": "3017203",
      "postDate": "10/14/2024 16:21:41",
      "content": "<p>In case someone wants to download the old JS competitiond dataset: <a href=\"https://www.kaggle.com/datasets/pedrocouto39/jane-street-market-train-data-best-formats\" target=\"_blank\">https://www.kaggle.com/datasets/pedrocouto39/jane-street-market-train-data-best-formats</a> </p>",
      "rawMarkdown": "In case someone wants to download the old JS competitiond dataset: https://www.kaggle.com/datasets/pedrocouto39/jane-street-market-train-data-best-formats",
      "votes": null
    },
    {
      "id": "3017212",
      "postDate": "10/14/2024 16:33:25",
      "content": "<p>Thank you for this dataset</p>",
      "rawMarkdown": "Thank you for this dataset",
      "votes": null
    },
    {
      "id": "3017989",
      "postDate": "10/15/2024 12:43:14",
      "content": "<p>wow, lots of deep learning approach as suppose to tree based one, thought that tree models (lgb, xgb, catboost) are pretty popular in tabular, and even some time series competition, anyone know why these top solution all opt for deep learning based method?</p>",
      "rawMarkdown": "wow, lots of deep learning approach as suppose to tree based one, thought that tree models (lgb, xgb, catboost) are pretty popular in tabular, and even some time series competition, anyone know why these top solution all opt for deep learning based method?",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3017140,
      "author_name": "yunsuxiaozi",
      "author_url": "",
      "post_date": "10/14/2024 14:44:58",
      "content": "<p>Nice to meet you here. I saw your notebook in the UM competition and think it was done very well.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 3017203,
      "author_name": "shiyili",
      "author_url": "",
      "post_date": "10/14/2024 16:21:41",
      "content": "<p>In case someone wants to download the old JS competitiond dataset: <a href=\"https://www.kaggle.com/datasets/pedrocouto39/jane-street-market-train-data-best-formats\" target=\"_blank\">https://www.kaggle.com/datasets/pedrocouto39/jane-street-market-train-data-best-formats</a> </p>",
      "votes": null,
      "replies": [
        {
          "id": 3017212,
          "author_name": "sumit08",
          "author_url": "",
          "post_date": "10/14/2024 16:33:25",
          "content": "<p>Thank you for this dataset</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 3017989,
      "author_name": "yuchen2066",
      "author_url": "",
      "post_date": "10/15/2024 12:43:14",
      "content": "<p>wow, lots of deep learning approach as suppose to tree based one, thought that tree models (lgb, xgb, catboost) are pretty popular in tabular, and even some time series competition, anyone know why these top solution all opt for deep learning based method?</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3017105": "As a student interested in quant finance and tabular data, this competition is a dream come true! My thanks to Jane Street and Kaggle!\n\nThe previous competition was the [Jane Street Market Prediction](https://www.kaggle.com/competitions/jane-street-market-prediction), with the 3 best available solutions being:\n\n- [*1st Place*](https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224348)\n- [*3rd Place*](https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224713)\n- [*10th Place*](https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/226837)\n\nBest of luck to all participants!",
    "3017140": "Nice to meet you here. I saw your notebook in the UM competition and think it was done very well.",
    "3017203": "In case someone wants to download the old JS competitiond dataset: https://www.kaggle.com/datasets/pedrocouto39/jane-street-market-train-data-best-formats",
    "3017212": "Thank you for this dataset",
    "3017989": "wow, lots of deep learning approach as suppose to tree based one, thought that tree models (lgb, xgb, catboost) are pretty popular in tabular, and even some time series competition, anyone know why these top solution all opt for deep learning based method?"
  },
  "source": "meta"
}