Times series deep learning model for trading.
experiment
is the configurations of everything. In theory, this should be reproducible and deterministic (maybe I need to add a seed in the json). The experiment determines the temporal framework used. Values can be for instance walk-forward backtest, cross-validation, standard (=train-val-test), robustness analysis, transfer learning and so onmarket
identifies the timeseries used and the logic used in calculating the trading costsagent
is the trading agent. In the first case,agent
issupervised
and has two parameters:predictor
andtrader
. Otherwise, the agent is RL based.
Please find our LaTeX documentation here
In order to run an experiment, run the following command in the terminal:
python pythia --run [SETTINGS_FILE]
For example:
python pythia --run data/experiments/chalvatzis/settings.json