license: cc-by-4.0
task_categories:
- reinforcement-learning
- tabular-classification
language:
- en
tags:
- synthetic
- finance
- defi
- crypto
- mcts
- trading-agents
- execution-intelligence
pretty_name: Solstice ARC-T DeFi Execution Intelligence
Solstice ARC-T DeFi Execution Intelligence (Sample)
Synthetic risk-to-execution decision loops for autonomous DeFi agents. This dataset captures 3,000 multi-step decision traces from autonomous trading agents operating in a simulated Decentralized Finance (DeFi) ecosystem.
Built by Solstice AI Studio as a free sample of a larger commercial pack. 100% synthetic — no real wallet addresses or on-chain history used.
What's in the box
Each record in the JSONL stream represents a full execution loop, including:
- Market State: Simulated oracle prices, liquidity depths, and volatility triggers.
- Risk Assessment: Whale movement signals and oracle staleness metrics.
- Agent Reasoning: MCTS-style strategy selection (Monte Carlo Tree Search) with success/failure probability weights.
- Execution Outcome: Final trade result including gas costs, slippage, and PnL.
Use Cases
- Autonomous Agent Training: Train models to select optimal trading strategies based on simulated market stress.
- Risk Model Evaluation: Benchmark protocol safety against extreme market scenarios and oracle failures.
- Anomaly Detection: Identify malicious or inefficient trading patterns in high-frequency DeFi telemetry.
Data Provenance
Generated using Solstice’s PhantasOS / SIMA simulation engine. The simulation uses MCTS (Monte Carlo Tree Search) to explore the decision space of a trading agent, recording both the chosen path and the explored alternatives.
Get the Full Pack
Scale this dataset to 1M+ decision loops, including cross-chain bridge logic and complex liquidity provider (LP) scenarios. www.solsticestudio.ai/datasets