• Personal study project for getting used to java spring
• Stock price prediction web site
• LSTM model trained for predict next day KOSPI close price
• Check site from website

result
result
result
result
result
result
result
result

• data

  • KOSPI close price prediction for next day
  • trained data KOSPI between '2014-01-01' to '2022-01-01'
  • test data KOSPI between '2022-01-01' to '2022-07-01'

• regression LSTM

  • model structure (LSTM, {'n_layers':1, 'bidirectional': True,"rnn_dropout":0.3,"fc_dropout":0.9})
  • mse loss 0.000741
  • When use it as category classification, up down over 0.2% increase than prev close price
    • expected earning when only buy true predicted : 0.15% per day (without transaction fee(tax))
    • expected earning when all buy : -0.10% per day (without transaction fee(tax))

     precision recall f1-score support 
     False 0.59 0.88 0.71 50376 
     True 0.46 0.14 0.21 35269 
     accuracy 0.58 85645 
     macro avg 0.52 0.51 0.46 85645 
    weighted avg 0.54 0.58 0.51 85645 
    

• Spring
  • Java 11
  • Spring boot 2.6.6
  • JPA, hibernate
  • Spring security
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  • lombok
  • h2
  • mariadb
  • redis
• Nuxt
  • pinia
  • billboard.js
  • vue-sweetalert2
• fastai
  • tsai

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• Key takeaways from Kaggle’s most recent time series competition
• Deep Learning With Weighted Cross Entropy Loss On Imbalanced Tabular Data Using FastAI
• How to Deal With Imbalanced Classification and Regression Data
• Delving into Deep Imbalanced Regression
• 불균형 데이터 해결하기, 주가 예측 프로젝트
• Stanford researchers have developed an AI model, StockBot