{ "id": 284883, "title": "[Compilation] - Winning Solutions of Previous Market Prediction Kaggle Challenges", "url": "/competitions/g-research-crypto-forecasting/discussion/284883", "author_name": "", "post_date": "2021-11-02T18:45:12.486007700Z", "votes": 30, "comment_count": 2, "views": 0, "content": "

Huge credit goes to @usharengaraju for compiling the original list on the jane street competition.
\nOriginal post: https://www.kaggle.com/c/jane-street-market-prediction/discussion?sort=votes
\nGo upvote it if you find this post useful!

\n

Some of the earlier market price prediction challenges in Kaggle are below:

\n

Optiver
\nCredit to the original post: https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275825

\n\n

The Winton Stock Market Challenge
\nhttps://www.kaggle.com/c/the-winton-stock-market-challenge/overview

\n\n

The Big Data Combine Engineered by BattleFin
\nhttps://www.kaggle.com/c/battlefin-s-big-data-combine-forecasting-challenge/overview

\n\n

Two Sigma Financial Modeling Challenge
\nTwo Sigma Financial Modeling Challenge Economic opportunity depends on the ability to deliver singularly accurate forecasts in a world of uncertainty. By accurately predicting financial movements, Kagglers can learn about scientifically-driven approaches to unlocking significant predictive capability.
\nhttps://www.kaggle.com/c/two-sigma-financial-modeling/overview

\n\n

Two Sigma: Using News to Predict Stock Movements
\nUsing News to Predict Stock Movements By analyzing news data to predict stock prices, Kagglers have a unique opportunity to advance the state of research in understanding the predictive power of the news. This power, if harnessed, could help predict financial outcomes and generate significant economic impact all over the world. At the end of stage one (model development), code will be \"frozen\" and the leaderboard will transition to show scores on future data.

\n

https://www.kaggle.com/c/two-sigma-financial-news/overview

\n\n

Bonus:
\nCredit: https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/249516

\n

A good article to understand the complex relationship between uncertainty and volatility: https://thehedgefundjournal.com/volatility-and-uncertainty/

\n

VUCA = Volatility, uncertainty, complexity and ambiguity.

\n

V = Volatility: the nature and dynamics of change, and the nature and speed of change forces and change catalysts.
\nU = Uncertainty: the lack of predictability, the prospects for surprise, and the sense of awareness and understanding of issues and events.
\nC = Complexity: the multiplex of forces, the confounding of issues, no cause-and-effect chain and confusion that surrounds organization.
\nA = Ambiguity: the haziness of reality, the potential for misreads, and the mixed meanings of conditions; cause-and-effect confusion.
\nExamining the impact of uncertainty on market dynamics is a challenging question for economists because no objective measure of uncertainty exists. Although in a general sense uncertainty is defined as the volatility of an unforecastable disturbance, the empirical literature to date has usually relied on proxies.

\n

Hope you found it useful.. go upvote the original posts! they deserve it!
\nHappy Kaggling!
\nCheers ^^

", "messages": [ { "id": "1568426", "postDate": "11/02/2021 18:45:12", "content": "

Huge credit goes to @usharengaraju for compiling the original list on the jane street competition.
\nOriginal post: https://www.kaggle.com/c/jane-street-market-prediction/discussion?sort=votes
\nGo upvote it if you find this post useful!

\n

Some of the earlier market price prediction challenges in Kaggle are below:

\n

Optiver
\nCredit to the original post: https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275825

\n\n

The Winton Stock Market Challenge
\nhttps://www.kaggle.com/c/the-winton-stock-market-challenge/overview

\n\n

The Big Data Combine Engineered by BattleFin
\nhttps://www.kaggle.com/c/battlefin-s-big-data-combine-forecasting-challenge/overview

\n\n

Two Sigma Financial Modeling Challenge
\nTwo Sigma Financial Modeling Challenge Economic opportunity depends on the ability to deliver singularly accurate forecasts in a world of uncertainty. By accurately predicting financial movements, Kagglers can learn about scientifically-driven approaches to unlocking significant predictive capability.
\nhttps://www.kaggle.com/c/two-sigma-financial-modeling/overview

\n\n

Two Sigma: Using News to Predict Stock Movements
\nUsing News to Predict Stock Movements By analyzing news data to predict stock prices, Kagglers have a unique opportunity to advance the state of research in understanding the predictive power of the news. This power, if harnessed, could help predict financial outcomes and generate significant economic impact all over the world. At the end of stage one (model development), code will be \"frozen\" and the leaderboard will transition to show scores on future data.

\n

https://www.kaggle.com/c/two-sigma-financial-news/overview

\n\n

Bonus:
\nCredit: https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/249516

\n

A good article to understand the complex relationship between uncertainty and volatility: https://thehedgefundjournal.com/volatility-and-uncertainty/

\n

VUCA = Volatility, uncertainty, complexity and ambiguity.

\n

V = Volatility: the nature and dynamics of change, and the nature and speed of change forces and change catalysts.
\nU = Uncertainty: the lack of predictability, the prospects for surprise, and the sense of awareness and understanding of issues and events.
\nC = Complexity: the multiplex of forces, the confounding of issues, no cause-and-effect chain and confusion that surrounds organization.
\nA = Ambiguity: the haziness of reality, the potential for misreads, and the mixed meanings of conditions; cause-and-effect confusion.
\nExamining the impact of uncertainty on market dynamics is a challenging question for economists because no objective measure of uncertainty exists. Although in a general sense uncertainty is defined as the volatility of an unforecastable disturbance, the empirical literature to date has usually relied on proxies.

\n

Hope you found it useful.. go upvote the original posts! they deserve it!
\nHappy Kaggling!
\nCheers ^^

", "rawMarkdown": "Huge credit goes to @usharengaraju for compiling the original list on the jane street competition. \nOriginal post: https://www.kaggle.com/c/jane-street-market-prediction/discussion?sort=votes\nGo upvote it if you find this post useful! \n\n**Some of the earlier market price prediction challenges in Kaggle are below:**\n\n**Optiver**\nCredit to the original post: https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275825\n- [2nd place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/274970 - This solution talks about the use of nearest neighbor features & reverse engineering the time ids.\n- [3rd place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275185 - An innovative & creative approach using graph neural networks (GNN)!\n- [12th place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275169. Also a really cool and innovative approach that models inter-stock relationships & also using custom features in their architecture.\n- [20th place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275543 - A solution which utilizes post processing.\n- [23rd place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275140 - This approach utilizes feature engineering from its group & also model ensembles. I also like how they show some of the ways they used scaling and transformations for building their models.\n- [32nd place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275806. An approach that shows how powerful an ensemble can be!\n- [37th place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275473\n\n\n\n**The Winton Stock Market Challenge**\nhttps://www.kaggle.com/c/the-winton-stock-market-challenge/overview\n- solution sharing thread : https://www.kaggle.com/c/the-winton-stock-market-challenge/discussion/18584\n\n**The Big Data Combine Engineered by BattleFin**\nhttps://www.kaggle.com/c/battlefin-s-big-data-combine-forecasting-challenge/overview\n- solution sharing thread: https://www.kaggle.com/c/battlefin-s-big-data-combine-forecasting-challenge/discussion/5966\n\n**Two Sigma Financial Modeling Challenge**\nTwo Sigma Financial Modeling Challenge Economic opportunity depends on the ability to deliver singularly accurate forecasts in a world of uncertainty. By accurately predicting financial movements, Kagglers can learn about scientifically-driven approaches to unlocking significant predictive capability.\nhttps://www.kaggle.com/c/two-sigma-financial-modeling/overview\n- solution sharing thread: https://www.kaggle.com/c/two-sigma-financial-modeling/discussion/29466\n- #4 Public, #13 Private Solution: https://www.kaggle.com/c/two-sigma-financial-modeling/discussion/29518\n\n**Two Sigma: Using News to Predict Stock Movements**\nUsing News to Predict Stock Movements By analyzing news data to predict stock prices, Kagglers have a unique opportunity to advance the state of research in understanding the predictive power of the news. This power, if harnessed, could help predict financial outcomes and generate significant economic impact all over the world. At the end of stage one (model development), code will be \"frozen\" and the leaderboard will transition to show scores on future data.\n\nhttps://www.kaggle.com/c/two-sigma-financial-news/overview\n\n- 5th place winners interview: https://medium.com/kaggle-blog/two-sigma-financial-modeling-code-competition-5th-place-winners-interview-team-best-fitting-279a493c76bd\n- 47th Place: https://www.kaggle.com/silvernine/very-simple-nn-model-market-data-only\n- 89th Place: https://www.kaggle.com/alluxia/lb-0-6326-tuned-xgboost-baseline\n- 111th Place: https://www.kaggle.com/charleslandau/iterative-approach\n- 117th Place: https://www.kaggle.com/yatzhash/news-features-without-headline-subjects\n- Post-competition thoughts https://www.kaggle.com/c/two-sigma-financial-news/discussion/102914\n\n\n**Bonus:**\nCredit: https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/249516\n\nA good article to understand the complex relationship between uncertainty and volatility: https://thehedgefundjournal.com/volatility-and-uncertainty/\n\nVUCA = Volatility, uncertainty, complexity and ambiguity.\n\nV = Volatility: the nature and dynamics of change, and the nature and speed of change forces and change catalysts.\nU = Uncertainty: the lack of predictability, the prospects for surprise, and the sense of awareness and understanding of issues and events.\nC = Complexity: the multiplex of forces, the confounding of issues, no cause-and-effect chain and confusion that surrounds organization.\nA = Ambiguity: the haziness of reality, the potential for misreads, and the mixed meanings of conditions; cause-and-effect confusion.\nExamining the impact of uncertainty on market dynamics is a challenging question for economists because no objective measure of uncertainty exists. Although in a general sense uncertainty is defined as the volatility of an unforecastable disturbance, the empirical literature to date has usually relied on proxies.\n\n\nHope you found it useful.. go upvote the original posts! they deserve it! \nHappy Kaggling! \nCheers ^^", "votes": null }, { "id": "1569865", "postDate": "11/03/2021 19:17:08", "content": "

Good post @yamqwe. I think the first link to the original post of @usharengaraju should be: https://www.kaggle.com/c/jane-street-market-prediction/discussion/199189

", "rawMarkdown": "Good post @yamqwe. I think the first link to the original post of @usharengaraju should be: https://www.kaggle.com/c/jane-street-market-prediction/discussion/199189", "votes": null }, { "id": "1575637", "postDate": "11/08/2021 14:38:08", "content": "

Great job!!!

", "rawMarkdown": "Great job!!!", "votes": null } ], "comments": [ { "id": 1569865, "author_name": "ajpass", "author_url": "", "post_date": "11/03/2021 19:17:08", "content": "

Good post @yamqwe. I think the first link to the original post of @usharengaraju should be: https://www.kaggle.com/c/jane-street-market-prediction/discussion/199189

", "votes": null, "replies": [] }, { "id": 1575637, "author_name": "pbokieee", "author_url": "", "post_date": "11/08/2021 14:38:08", "content": "

Great job!!!

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1568426": "Huge credit goes to @usharengaraju for compiling the original list on the jane street competition. \nOriginal post: https://www.kaggle.com/c/jane-street-market-prediction/discussion?sort=votes\nGo upvote it if you find this post useful! \n\n**Some of the earlier market price prediction challenges in Kaggle are below:**\n\n**Optiver**\nCredit to the original post: https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275825\n- [2nd place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/274970 - This solution talks about the use of nearest neighbor features & reverse engineering the time ids.\n- [3rd place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275185 - An innovative & creative approach using graph neural networks (GNN)!\n- [12th place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275169. Also a really cool and innovative approach that models inter-stock relationships & also using custom features in their architecture.\n- [20th place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275543 - A solution which utilizes post processing.\n- [23rd place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275140 - This approach utilizes feature engineering from its group & also model ensembles. I also like how they show some of the ways they used scaling and transformations for building their models.\n- [32nd place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275806. An approach that shows how powerful an ensemble can be!\n- [37th place] - https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/275473\n\n\n\n**The Winton Stock Market Challenge**\nhttps://www.kaggle.com/c/the-winton-stock-market-challenge/overview\n- solution sharing thread : https://www.kaggle.com/c/the-winton-stock-market-challenge/discussion/18584\n\n**The Big Data Combine Engineered by BattleFin**\nhttps://www.kaggle.com/c/battlefin-s-big-data-combine-forecasting-challenge/overview\n- solution sharing thread: https://www.kaggle.com/c/battlefin-s-big-data-combine-forecasting-challenge/discussion/5966\n\n**Two Sigma Financial Modeling Challenge**\nTwo Sigma Financial Modeling Challenge Economic opportunity depends on the ability to deliver singularly accurate forecasts in a world of uncertainty. By accurately predicting financial movements, Kagglers can learn about scientifically-driven approaches to unlocking significant predictive capability.\nhttps://www.kaggle.com/c/two-sigma-financial-modeling/overview\n- solution sharing thread: https://www.kaggle.com/c/two-sigma-financial-modeling/discussion/29466\n- #4 Public, #13 Private Solution: https://www.kaggle.com/c/two-sigma-financial-modeling/discussion/29518\n\n**Two Sigma: Using News to Predict Stock Movements**\nUsing News to Predict Stock Movements By analyzing news data to predict stock prices, Kagglers have a unique opportunity to advance the state of research in understanding the predictive power of the news. This power, if harnessed, could help predict financial outcomes and generate significant economic impact all over the world. At the end of stage one (model development), code will be \"frozen\" and the leaderboard will transition to show scores on future data.\n\nhttps://www.kaggle.com/c/two-sigma-financial-news/overview\n\n- 5th place winners interview: https://medium.com/kaggle-blog/two-sigma-financial-modeling-code-competition-5th-place-winners-interview-team-best-fitting-279a493c76bd\n- 47th Place: https://www.kaggle.com/silvernine/very-simple-nn-model-market-data-only\n- 89th Place: https://www.kaggle.com/alluxia/lb-0-6326-tuned-xgboost-baseline\n- 111th Place: https://www.kaggle.com/charleslandau/iterative-approach\n- 117th Place: https://www.kaggle.com/yatzhash/news-features-without-headline-subjects\n- Post-competition thoughts https://www.kaggle.com/c/two-sigma-financial-news/discussion/102914\n\n\n**Bonus:**\nCredit: https://www.kaggle.com/c/optiver-realized-volatility-prediction/discussion/249516\n\nA good article to understand the complex relationship between uncertainty and volatility: https://thehedgefundjournal.com/volatility-and-uncertainty/\n\nVUCA = Volatility, uncertainty, complexity and ambiguity.\n\nV = Volatility: the nature and dynamics of change, and the nature and speed of change forces and change catalysts.\nU = Uncertainty: the lack of predictability, the prospects for surprise, and the sense of awareness and understanding of issues and events.\nC = Complexity: the multiplex of forces, the confounding of issues, no cause-and-effect chain and confusion that surrounds organization.\nA = Ambiguity: the haziness of reality, the potential for misreads, and the mixed meanings of conditions; cause-and-effect confusion.\nExamining the impact of uncertainty on market dynamics is a challenging question for economists because no objective measure of uncertainty exists. Although in a general sense uncertainty is defined as the volatility of an unforecastable disturbance, the empirical literature to date has usually relied on proxies.\n\n\nHope you found it useful.. go upvote the original posts! they deserve it! \nHappy Kaggling! \nCheers ^^", "1569865": "Good post @yamqwe. I think the first link to the original post of @usharengaraju should be: https://www.kaggle.com/c/jane-street-market-prediction/discussion/199189", "1575637": "Great job!!!" }, "source": "meta" }