Building V6 With Freqtrade and FreqAI
V6 was designed to reduce the amount of custom trading infrastructure we had to invent while preserving control over the research process. Freqtrade handles the trading framework; FreqAI supplies the ML workflow; our experiment defines what is trained, traded and measured.
Current experiment architecture
Why Freqtrade/FreqAI
The move was not an assumption that FreqAI is profitable. It was an engineering decision: use a mature framework for execution, state and ML integration so the experiment can focus more clearly on features, targets, model behavior and forward results.
Infrastructure boundary
The trading VPS contains execution state and credentials. The public website is separate. A small exporter converts experiment results into sanitized metrics, which are pushed over HTTPS to a read-only public data path.
No exchange API key, raw production configuration, position quantity or bot-control endpoint is required by the website.
What we measure
Closed/open trades, realized PAPER PnL, win/loss counts, expectancy, profit factor, closed-PnL drawdown, a cumulative realized-PnL path and recent sanitized trades. The sample size remains visible because attractive early metrics can be statistically weak.