kaggle-two-sigma-winner
所属分类：Leetcode/题库
开发工具：Jupyter Notebook
文件大小：12267KB
下载次数：0
上传日期：2022-08-09 12:29:01
上 传 者：
sh-1993
说明： Kaggle Two-Sigma二等奖获奖代码<https:www.Kaggle.com c Two-Segma-财经新闻>
文件列表:
.idea (0, 2022-08-09)
.idea\inspectionProfiles (0, 2022-08-09)
.idea\inspectionProfiles\profiles_settings.xml (174, 2022-08-09)
.idea\kaggle_two_sigma_git.iml (284, 2022-08-09)
.idea\misc.xml (196, 2022-08-09)
.idea\modules.xml (292, 2022-08-09)
.idea\workspace.xml (328, 2022-08-09)
SETTINGS.json (1102, 2022-08-09)
alphalens_plots.ipynb (1750303, 2022-08-09)
ann.py (5559, 2022-08-09)
backtest.py (5372, 2022-08-09)
data (0, 2022-08-09)
data\fake_marketdata_beginning.csv (674738, 2022-08-09)
data\marketdata_sample.csv (18991, 2022-08-09)
data\news_sample.csv (43079, 2022-08-09)
data\submission_sample.csv (33137781, 2022-08-09)
directory_structure.txt (72, 2022-08-09)
documentation (0, 2022-08-09)
documentation\Two Sigma Using News to Predict Stock Movements Kaggle Winner Call - v2.pdf (565239, 2022-08-09)
documentation\Two Sigma Using News to Predict Stock Movements.pdf (354921, 2022-08-09)
entry_points.md (681, 2022-08-09)
features_engineering.ipynb (32824, 2022-08-09)
generate_fake_marketdata.py (2401, 2022-08-09)
logger.py (1460, 2022-08-09)
market-data-nn-baseline_original_code.ipynb (13209, 2022-08-09)
models (0, 2022-08-09)
models\model_original.hdf5 (381536, 2022-08-09)
predict.py (7378, 2022-08-09)
prepare_data.py (7210, 2022-08-09)
read_settings.py (713, 2022-08-09)
requirements.txt (42, 2022-08-09)
train.py (4919, 2022-08-09)
1. The hardware you used: CPU specs, number of CPU cores, memory, GPU specs, number of GPUs.
Online kaggle environment: 4 CPU cores, 16 Gigabytes of RAM, No GPU.
2. OS/platform you used, including version number.
Latest kaggle docker: https://github.com/Kaggle/docker-python
3. Any necessary 3rd-party software, including version numbers, and installation steps. This can be provided as a Dockerfile instead of as a section in the readme.
No
4. How to prepare data
Run prepare_data.py
5. How to train your model
Run train.py
6. How to make predictions on a new test set.
Run predict.py
7. Important side effects of your code. For example, if your data processing code overwrites the original data.
On data cleaning code overwrites some outliers.
8. Key assumptions made by your code. For example, if the outputs folder must be empty when starting a training run.
No
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