epsil

Application of Machine Learning in Finance Domain like Stocks/Assets trend prediction, Anomaly detection, Volatility Estimate.

Finance Algorithms:

1. Stock Trend Prediction as a Classification Problem with DecisionTreeClassifier and GradientBoostingClassifier.
2. Forex Trading using LSTM, GRU and CNN.
3. Asset Allocation using One Step Prediction from Random Forest and Linear Regressor and using Buy and Hold Strategy as a baseline.
4. Option Analysis Analysis of various Put Call Options through different algorithms.

Forex Trading Results (Regression Formulation)

Default Values:

Feature	Value
Sequence length	60
Number of Layers	4
Droput Value	0.2
Sequence length	60

Performance

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

Forex Trading Results (Classification Formulation)

Default Values:

Feature	Value
Sequence length	60
Number of Layers	4
Droput Value	0.2
Sequence length	60

Performance

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

Asset Allocation Results

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

1. Anomaly Detection using Autoencoders on a private datatset.
2. Anomaly Detection using Isolation Forest(Unsupervised Learning) on a simple dummy dataset