V6 FreqAI PAPER Trading Experiment
An ongoing machine-learning experiment that retrains on incoming market data and trades BTC, ETH and SOL in simulated execution. The purpose is to observe the system forward—not to turn a small winning sample into a profitability claim.
Current snapshot
*Win rate without sample size, payoff distribution and drawdown can be misleading.
Cumulative realized PAPER PnL
Recent closed PAPER trades
What we are testing
V6 tests whether a retrained ML workflow can produce useful forward behavior after the entire pipeline is included: market data, features, model training, signal generation, position management and simulated execution. We are interested in stability and net outcomes, not just prediction accuracy.
Multiple crypto markets expose the system to overlapping market risk.
Signals and execution are evaluated on short-horizon market data.
The model learns from a rolling recent sample rather than a fixed historical dataset.
New observations periodically produce a refreshed model.
Experiment pipeline
The public website receives only sanitized aggregate metrics. Exchange credentials and bot controls remain on separate trading infrastructure.
How we judge the experiment
Net realized PnL, payoff distribution, drawdown and losing sequences—not win rate in isolation.
Trade frequency, simultaneous exposure, long/short behavior and changes across market conditions.
Whether periodic retraining improves, destabilizes or simply changes behavior.
Data freshness, process continuity and whether the deployed system matches the intended version.
What the current result does not prove
A small sample can produce extreme win rates by chance. PAPER fills do not reproduce all live slippage, latency and liquidity constraints. Future trades can reverse the current result, and retraining can change behavior. For those reasons, this page reports observations rather than extrapolating an expected return.
Why we keep V4 and V4.4 visible
A new model should not erase the systems that preceded it. Keeping earlier versions visible reduces survivorship bias and makes it harder to present only the experiment that currently looks best.
Compare V4, V4.4 and V6 →How this version was built
The build log explains why V6 followed the custom adaptive engine and how Freqtrade, FreqAI, OKX and the sanitized metrics pipeline fit together.
Read the V6 build log →