All financial transactions, including monthly subscriptions and on-demand token top-ups, are securely processed via Stripe. We support all major credit cards, Apple Pay, and Google Pay for seamless infrastructure scaling.

Once your strategy is published and generating revenue from other users, you can receive payouts by linking your preferred payment method in the Creator Dashboard. Payouts are distributed on a monthly cycle after reaching a minimum threshold.

Tokens represent the computational power required for OCULUS to iterate on your strategy. Each time you ask the AI to "Piviot" or "Re-optimize" a strategy, tokens are consumed to power the thousands of backtest simulations and data-point references required.

Correct. OCULUS is built on a natural language interface. If you can articulate your market thesis in English, the engine can architect the quantitative code, manage the Docker containers, and handle the API execution for you.

Sovereignty is a core pillar of Oculus. Your strategies are stored in encrypted environments. Unless you explicitly choose to publish your strategy to the Marketplace, your logic remains your private intellectual property.

When you deploy a strategy for personal use, OCULUS wraps the algorithm in a sandboxed Docker container. This ensures your code is isolated, secure, and can execute 24/7 without being affected by external infrastructure changes.

OCULUS references thousands of data points including historical price action, macro-economic indicators, sector correlations, and micro-structure order flow to ensure every iteration is battle-tested against multiple market regimes.

Yes. You can prompt OCULUS to trade specific baskets or sectors (e.g., "The top 10 S&P Tech stocks"). The AI will manage the weightings and correlations across the entire sector automatically.

If a re-optimization cycle fails to meet your defined drawdown or return parameters, OCULUS logs the failure, analyzes why the target was missed, and prepares a new "Chain of Thought" for a subsequent iteration.

On average, a deep strategy iteration takes roughly 60 minutes. This time allows the engine to run exhaustive simulations across years of historical data to ensure mathematical validity.