https://github.com/rbhatia46/reliance-stock-prediction-using-svm
Predicting buy/sell of Reliance Stock using a Support Vector Machine on top of commonly used technical indicators.
https://github.com/rbhatia46/reliance-stock-prediction-using-svm
Last synced: 6 months ago
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Predicting buy/sell of Reliance Stock using a Support Vector Machine on top of commonly used technical indicators.
- Host: GitHub
- URL: https://github.com/rbhatia46/reliance-stock-prediction-using-svm
- Owner: rbhatia46
- Created: 2019-09-16T11:15:02.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-02-03T14:18:40.000Z (over 3 years ago)
- Last Synced: 2025-03-25T00:04:53.567Z (7 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 70.3 KB
- Stars: 22
- Watchers: 2
- Forks: 8
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# SVM for predicting buy/sell of Reliance Stock
Comparing different SVM Kernels, specifically the linear kernel, polynomial kernel, and the Radial Basis Function Kernel, in their effectiveness when applied to predict buy/sell of Reliance Stock. A much detailed approach can be found on the accompanying [Medium article](https://medium.com/@e22aafa7d95/abb494d9f45f)
## Model details
Featuresets: various technical indicators for Reliance Stock
Labels: - Buy/Sell
- Buy - if price is predicted to rise
- Sell - of price is predicted to drop
### Results (Accuracy)
- Linear kernel: 51.43%
- Polynomial Kernel: 53.15%
- Radial Basis Function Kernel: 52.00%
- Random Model Accuracy : 33%
### Interesting aspects for improvement:
- Effects of feature standardization on SVMs
- Predictability of certain technical indicators, can be studied using Hypothesis Testing
- Use Sentiment Analysis and news on Reliance Stock/Stocks in your portfolio.
- Use more number of technical indicators(since SVM works well with high-dimensional data, you can easily add more features without worrying much about the curse of dimensionality).
- Apply the same on a diversified portfolio of stocks rather than a single stock.
- Find a domain-specific kernel, which can give the performance a significant boost, i.e., a kernel which works well for finance/time-series data, this needs some research.
- Fine-tune the existing SVM Architecture (use RandomSearch rather than GridSearch to make life easier)
- Interpret your model with model agnostic techniques (such as LIME)to understand any bias(if it exists) and understand which feature is contributing more to your prediction.
- Use more data(even more than 10 years of data) to obtain much better results, which of course comes at the cost of more time required to train the SVM.
- Try other classifiers, such as KNN, which can be quite similar to RBF-based SVM to see if there is any improvement in the performance.