Stock recommandation via machine learning algorithms

A stock investment assistant tool which utilized supervised machine learning models such as Logistic Regression, Random Forest, and Support Vector Machine to predict the stock’s 60 days’ return rate. If a specific stock outperformed the average return rate, the model would recommend to hold.

Tools

• Sklearn
• Tushare API
• multiprocessing-python based parallelism

How to use?

• Step1: Run 0-Get Fina Indicators Data.py to get financial indicator data from 2019-Q1 to 2020-Q1.
• Step 2: Calculate cumulated ROI for each stock, imputation, train test split by running 1-Dataset Construction.py.
• Step 3: Get the statistics of features by running 2-Dataset Description.py
• Step 3: Train machine learning models
  • 3-LG.py
  • 3-RF.py
  • 3-SVM.py
• Step 4: Testing via 4-Testing.py

Jupyter Notebooks for fine-tunning

• LG Fine Tunning-checkpoint.ipynb
• RF Fine Tunning-checkpoint.ipynb