https://github.com/johnberroa/randomforest-stockpredictor
Utilizing the ensemble method of random forests to predict stock prices.
https://github.com/johnberroa/randomforest-stockpredictor
ensemble prediction random-forest stock-market
Last synced: about 1 year ago
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Utilizing the ensemble method of random forests to predict stock prices.
- Host: GitHub
- URL: https://github.com/johnberroa/randomforest-stockpredictor
- Owner: johnberroa
- Created: 2017-05-29T08:35:34.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2018-02-19T22:25:25.000Z (over 8 years ago)
- Last Synced: 2025-03-26T03:51:16.483Z (about 1 year ago)
- Topics: ensemble, prediction, random-forest, stock-market
- Language: Jupyter Notebook
- Size: 28.6 MB
- Stars: 10
- Watchers: 1
- Forks: 5
- Open Issues: 2
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Metadata Files:
- Readme: README.md
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README
# Random Forest Stock Predictor
Utilizing the ensemble method of random forests to predict stock prices, based on the results of Khaidem, Saha, & Dey (2016).
Group course project for *Ensemble Methods of Machine Learning*, summer semester 2017 at the University of Osnabrück.
### Usage
1. Clone repo with: \
```git clone https://github.com/johnberroa/RandomForest-StockPredictor.git```
2. Use the [Technical Analysis Notebook.ipynb](Technical%20Analysis%20Notebook.ipynb) to generate indicators by running the whole notebook.
This will save them to the file ```data_preprocces.csv```.
3. Next run the [Random Forest Notebook.ipynb](Random%20Forest%20Notebook.ipynb). The Results can be found in the results directory.