Machine Learning project to aproximate trend changes in forex environment using timeseries-forecasting methods Analysed DataSets:
EURUSD (1h candles)
Goal&Summary:
Comparison of stock price trends' forecasting for different ML approaches and models. Focus at
- Data aquisition
Development: comparison performances for different candle' lenghts and utilizing their complementation
- Data preprocessing
- Extension of data dimensionality by including economical analysis indicators
- RSI,
- Moving Avarage,
- Long time trends,
- Model parameters' assesement and deployment of first dep[loyment of models
- Parameter's evaluation and over-/under- fitting analysis
- Statistical analysis and dimensionality reduction
- Comparison of results for separate models
- Integration of multiple models
- Evaluation of obtained results