V7: Building Our Next Self-Learning Trading System
Development has started on V7. It is not intended to be simply a more complicated version of V6. It asks a different question: can a trading system learn how to make decisions rather than simply learn how to predict the market?
Why V7 exists
Our previous experiments moved through rule-based systems, adaptive branches and the current V6 FreqAI experiment. V7 explores a more autonomous architecture in which more of the decision process can adapt as new market data arrives.
That means learning not only when a market move may occur, but whether a trade should exist at all, how the current market regime should affect a decision, and how risk and exits should respond.
What we are designing
The initial architecture is being built around several research goals:
- Continuous learning from new market data.
- Adaptation to changing market regimes.
- An explicit NO-TRADE decision rather than forcing exposure.
- Independent evaluation of different decision policies.
- Risk-aware position and exit decisions.
- Strict separation between training, evaluation and execution.
- Transparent PAPER results once the system becomes operational.
Simulation first
V7 will begin entirely in a simulated environment. No real capital is part of the development phase. The objective is to observe behavior, failure modes and adaptation before considering any further stage.
What we do not know yet
We do not know whether this architecture will outperform V6, whether it will produce a durable edge, or whether it will work at all. That uncertainty is the experiment.
As development progresses, we intend to publish major architecture decisions, changes between iterations, failures and eventually its PAPER trading results. Failed branches will remain part of the record.