FreqAI Setup: From Installation to First Model
Treat the first model as an experiment, not a finished trading system. The useful workflow is reproducible training followed by forward PAPER observation.
Define the experiment first
Choose markets, timeframe, prediction target, training window and retraining cadence before optimizing results. Record these settings so later model versions remain comparable.
A setup that can be tested
Changing several of these layers at once makes attribution difficult. Version the complete experiment, not just the model file.
Features and target
Features describe the information available to the model; the target defines what it is asked to predict. Leakage between future data and training inputs can make historical results meaningless, so time ordering matters.
Training and retraining
FreqAI can periodically retrain models as new observations arrive. More frequent retraining is not automatically better: it increases compute cost and can make behavior unstable if the data window or target is poorly specified.
Move to PAPER evaluation
Track net PnL after modeled costs, trade count, drawdown, win/loss distribution and behavior by market regime. Preserve failed versions instead of silently replacing them.
Example from our lab
Our current V6 configuration tests BTC, ETH and SOL on 5-minute data with a 30-day training window and six-hour retraining cadence.
Follow the experiment →