Stock Price Prediction Competition @DACON

🔗 Refer to blog post for more experimentation notes

Goal

• Predicting the Closing price of 376 Korean public companies
• Public Leaderboard was based on the Closing price of 2021-09-01 ~ 2021-09-06
• Private Leaderboard was based on the Closing price of 2021-09-27 ~ 2021-10-01.
• No limitations for the data source, but there were limitations for submissions per day.

Result

🥈 NMAE score of 4.44 ranked the 2nd place / 29 teams

Models' Performance

ElasticNetCV model’s NMAE score outperformed Baseline’s score by 30%. Competition's criterion is based on NMAE. Criterion function was constructed as following:

def NMAE(true_df, pred_df_input):
    """ grading criterion for public leader board """
    return (abs(true_df_copy - pred_df_input) / true_df_copy * 100).iloc[:5].values.mean()

Model	NMAE (09-06 ~ 09-10)	NMAE (09-15 ~ 09-24)	NMAE (09-27 ~ 10-01)
ElasticNetCV	3.02	2.93	4.31
ARIMA(0,1,1)	3.03	-	-
ElasticNet	3.12	-	-
XGBoost	3.87	4.22	-
Linear Regression (Baseline Code)	4.03	-	6.42
RFRegressor	4.11	-	-
pmdARIMA	8.81	-	-

Model was further evaluated on November which marked similar performance as it was in September.

Installation

TA-Lib

• 🔗 Install TA-Lib on Google Colab

• Install TA-Lib on MacOS

  brew install tal-lib
  pip3 install TA-Lib

• Install TA-Lib on Windows Download ta-lib-0.4.0-msvc.zip and unzip to C:\ta-lib

  pip3 install TA-Lib
  

• Install TA-Lib on Linux Download ta-lib-0.4.0-src.tar.gz

  untar and cd
  ./configure --prefix=/usr
  make
  sudo make install

Other Dependencies

pip3 install -r requirements.txt

How to run

python main.py

Authorship

• @sanghoeKim
  • Created the dataset, chose and added derivative data.
  • Constructed training process for the ElasticNet model.
  • Set the loss function for the evaluation.
  • Tested XGBRegressor, DNN and ElasticNet.
• @snoop2head ✋
  • Applied cross validation by using ElasticNetCV model.
  • Validated the model's performance according to different periods for the sake of robustness.
  • Completed the model's inference for the evaluation period.
  • Tested ARIMA, RandomforestRegressor and ElasticNetCV.
• @tjy3090
  • Provided knowledge for stock market behavior.
  • Tested ARIMA.