Skip to content
Predicting buy/sell of Reliance Stock using a Support Vector Machine on top of technical indicators commonly used in Finance.
Jupyter Notebook
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
SVM to predict Reliance Stock.ipynb

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

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.
You can’t perform that action at this time.