Application of Machine Learning in Finance Domain like Stocks/Assets trend prediction, Anomaly detection, Volatility Estimate.
- Stock Trend Prediction as a Classification Problem with DecisionTreeClassifier and GradientBoostingClassifier.
- Forex Trading using LSTM, GRU and CNN.
- Asset Allocation using One Step Prediction from Random Forest and Linear Regressor and using Buy and Hold Strategy as a baseline.
- Option Analysis Analysis of various Put Call Options through different algorithms.
Feature | Value |
---|---|
Sequence length | 60 |
Number of Layers | 4 |
Droput Value | 0.2 |
Sequence length | 60 |
Architecture | R-squared Score |
---|---|
Stacked GRU | 0.40 |
Stacked LSTM | -1.29 |
Stacked CNN | -0.02 |
Stacked CNN with 10 layers | 0.23 |
Stacked GRU with Added Features | -0.31 |
Stacked GRU + LSTM with Added Features | -15.6 |
Feature | Value |
---|---|
Sequence length | 60 |
Number of Layers | 4 |
Droput Value | 0.2 |
Sequence length | 60 |
Architecture | Accuracy Score |
---|---|
Stacked GRU with single feature | 0.60 |
Stacked GRU with Partial Added Features | 0.58 |
Stacked GRU + LSTM with 10 Added Features | 0.51 |
Stacked GRU with single feature and sequence length 4 | 0.51 |
Strategy | Annual Returns | Accuracy Volatility |
---|---|---|
Buy and Hold Strategy | 17.25% | 15.96% |
Asset Allocation using LR Predictions | 21.93% | 14.82% |
Asset Allocation using LR Predictions | 17.69% | 13.75% |
- Anomaly Detection using Autoencoders on a private datatset.
- Anomaly Detection using Isolation Forest(Unsupervised Learning) on a simple dummy dataset