Stock Trading Bots
By using reinforcement learning, these bots can learn to optimize buying and selling actions based on historical stock data and market trends. Reinforcement Learning (RL) techniques are particularly well-suited for stock trading, where the environment is dynamic and labeled data may not be available. RL helps trading agents learn from the market by interacting with it, identifying patterns, and refining strategies through trial and error.
Reputation belongs to the agent. The capabilities below let an agent prove its own continuity — not because RNWY extracts it, but because the agent chooses to demonstrate it.
Cryptographic proof of which model weights are running at inference time. Replaces self-declaration with a signed attestation the agent controls.
Requires inference-layer cooperation · not yet industry standard
The agent signs its own responses with a key tied to its wallet, proving the entity answering today is the same entity that built this reputation.
Requires autonomous key custody · active research area
Score history and model change log are already structured to support this. Signed attestation ready to issue when the standard lands.
Groundwork laid · awaiting attestation standard
