EURAUD BYILLISH 120PIPS

Define Your Data:

Historical EUR/AUD price data (open, high, low, close) at a suitable time frequency (e.g., hourly or daily).
Include relevant covariates such as macroeconomic indicators, market sentiment, or other correlated currency pairs.

Select a Methodology:

Statistical Models:
ARIMA/SARIMA: Suitable for stationary series with clear trends or seasonality.
Exponential Smoothing (ETS): Effective for capturing trends and seasonality in time series data.
Machine Learning Models:
Gradient Boosting (e.g., XGBoost, LightGBM): Useful for incorporating additional features (macro data, other pairs).
Recurrent Neural Networks (RNNs) or Temporal Convolutional Networks (TCNs): Effective for capturing complex temporal dependencies.
Hybrid Models:
Combine ARIMA for trend extraction and machine learning for residual predictions.

Feature Engineering:

Create lagged features to capture momentum and moving averages.
Include RSI, MACD, Bollinger Bands, or other technical indicators as additional inputs.
Incorporate external data, such as economic reports or central bank policy announcements.

Model Training and Validation: