When creating Deep Learning Algo Trading systems based on history price data of a certain time period you can choose between several approaches to establish a model creation process:
- Splitting the complete history in exactly three sets (training, validation, test) and creating one model which you will use for your trading (Spoiler: Bad idea)
- Iterating over different timeframe sizes for the three datasets (training, validation, test) and this way only using a certain percentage of the available history to create exactly one model (as in 1.)
- Based on a fixed size of the three dataset timeframes (for training, validation, test) you create a shifting series of models and rate the series result instead of rating the result of a single model
- Based on a fixed size for the test dataset frame you create a series of models with variable training and validation dataset timeframe sizes. As size for the current training and test dataset timeframe size you select the training / validation timeframe combination with the highest validation-test result correlation in the previous period. The test dataset timeframe size defines the rhythm of model deployments.