Forecasting Euro Exchange Rates

Forecasting euro exchange rates

Execution of pipeline

python main.py -a import_data

python main.py -a train

python main.py -a predict

python main.py -a recommendations

Import data

Data is imported from the European Central Bank via the Quandl API

This step also generates multiple features like rolling average, rolling standard deviations, lags, etc. The target variable is computed as exchange rate in 7 days over current exchange rate. This is split in three categories:

return < -0.5%
return between -0.5% and 0.5%
return > 0.5%

Train model

The model consists of a deep and wide neural network. There is a single model that uses the exchange rate of all currencies. The categorical variable representing the currency is captures by he 'wide' features in the neural network.

Generate recommedations

Recommend a buy/sell acction based on model's predictions. Recommen 'buy' only if proability of increase of rate is much larger than probability of decrease.

ToDo

Add sklearn models (logistic regression, random forest, XGBoost)
Send recommendations to email
Run univariate data analysis. Compute how good is each individual variable
Try recurrent neural networks (LSTM)
When creating recommendations take into account the base model. Do not recommend an asset if model's probabilities are similar to baseline probabilities.
Backtesting

yaroslavmar / forecasting