solsticestudioai/arc-t-defi-pack

ARC-T DeFi Decision Telemetry Pack (Sample)

A synthetic DeFi risk-to-execution decision-telemetry dataset for autonomous-trading research, on-chain anomaly detection, and reinforcement-learning environments. Each row is a complete risk-to-execution lifecycle: a triggering market anomaly, market-state snapshot, adversarial-planner reasoning (MCTS branch count + winning strategy), step-by-step execution chain with per-step gas / slippage / latency telemetry, execution summary, and final decision outcome with PnL delta.

Built by SolsticeAI as a free sample of a larger commercial pack. 100% synthetic. No real trades, wallets, addresses, or on-chain identities. Safe for model training, competitive evaluation, and public benchmark work.

What is included

File	Rows	Format	Purpose
arc_t_defi_sample.parquet	10,000	Parquet	Columnar, typed, best for analytics
arc_t_defi_sample.jsonl	10,000	JSON Lines	Streaming / LLM training friendly

Source pack: 100K-lifecycle corpus (production pack scales to 2.5M+)
This sample: 10,000 lifecycles, stratified 2,500 per decision outcome
Outcome classes: alpha_captured, risk_mitigated, slippage_loss, execution_failed
Chains covered: ethereum, arbitrum, solana, optimism
Protocols covered: Aave-V3, Uniswap-V3, Curve-Fi, GMX, MakerDAO, Drift, Lido, Kamino, Jupiter, Orca, Marinade, Synthetix, Meteora
Risk triggers: oracle staleness, TVL crash, whale dump, governance attack, bridge congestion, funding/borrow-rate dislocation, liquidity fragmentation, redemption run, liquid-staking dislocation

Record structure

Each record is one risk-to-execution lifecycle with 7 top-level fields:

Field	Type	Contents
schema_version	string	Pack schema version (1.0.0-arc-t-sample)
event	struct	id, trace_id, timestamp, decision_outcome, pnl_delta_usd
risk_context	struct	trigger, protocol, chain, impacted_asset, anomaly_signature, nested market_state (severity, volatility_regime, liquidity_band, oracle_age, venue divergence, price impact, notional, 1h vol, etc.)
agent_reasoning	struct	engine, winning_strategy, confidence_score, mcts_branches
correlated_telemetry	list	Ordered component chain (ARES, FRACTAL, ARGUS, SENTINEL, …) with per-step latency, slippage, gas price, priority fee, route hops, node provider
execution_summary	struct	strategy, total execution time, gas cost, average slippage
genetic_optimizer_feedback	struct	fitness_score_update, parameter_drift

See SCHEMA.md for the full nested field breakdown.

Why this dataset is useful

Most public on-chain datasets are either raw block-level trace data or narrow single-protocol slices. This pack is shaped around what autonomous-trading and on-chain-risk teams actually need to train decision models:

• Complete risk-to-execution lifecycles rather than isolated transactions
• Balanced outcome classes across winning and losing execution states
• Adversarial reasoning trace (strategy + MCTS branch count + confidence) alongside the telemetry
• Per-step gas, slippage, and latency signals to train execution-aware policies
• Multi-chain, multi-protocol coverage for generalization across venues
• PnL-linked outcomes for reward shaping in RL environments
• Stable schema suitable for backtesting, RL gym integration, and dashboarding

Typical use cases

• Autonomous DeFi agent training (RL and supervised)
• On-chain anomaly detection and risk-trigger classification
• Execution-strategy optimization (gas / slippage / route)
• Arbitrage and cross-venue routing model development
• Market-microstructure and liquidity-state modeling
• LLM fine-tuning on execution narratives and decision rationale
• Benchmarking MCTS strategy selection under uncertainty
• Gym environment authoring for multi-chain trading

Quick start

import pandas as pd
import pyarrow.parquet as pq

df = pq.read_table("arc_t_defi_sample.parquet").to_pandas()

# Outcome distribution (stratified balanced)
print(df["event"].apply(lambda e: e["decision_outcome"]).value_counts())

# Average PnL by strategy
df["strategy"] = df["agent_reasoning"].apply(lambda r: r["winning_strategy"])
df["pnl"] = df["event"].apply(lambda e: e["pnl_delta_usd"])
print(df.groupby("strategy")["pnl"].mean().round(2))

# Chain vs outcome cross-tab
df["chain"] = df["risk_context"].apply(lambda r: r["chain"])
df["outcome"] = df["event"].apply(lambda e: e["decision_outcome"])
print(pd.crosstab(df["chain"], df["outcome"]))

Streaming form:

import json

with open("arc_t_defi_sample.jsonl") as f:
    for line in f:
        lifecycle = json.loads(line)
        # one risk-to-execution lifecycle per line

Responsible use

This dataset is intended for research, prototyping, and defensive / monitoring use cases around autonomous DeFi systems: anomaly detection, strategy evaluation, execution-policy training, and RL environments. It contains synthesized market states, execution traces, and decision outcomes — it does not contain real wallet addresses, real transaction hashes, real pool reserves, or any live on-chain state. Do not deploy policies trained solely on this synthetic data to real capital without independent validation against live data.

License

Released under CC BY 4.0. Use freely for research, RL experiments, education, and commercial prototyping with attribution.

Get the full pack

This Hugging Face repo is a 10K-lifecycle sample. The production pack scales to 2.5M+ lifecycles with expanded chain and protocol coverage, finer-grained market-state regimes, campaign-linked multi-step attack sequences, MEV-aware telemetry, richer step-level component traces, parquet + JSONL + gym-compatible formats, and buyer-specific variants.

Self-serve (Stripe checkout):

• Sample Scale tier — $5,000 — ~25K records, one subject, 72-hour delivery.

Full pack + enterprise scope:

• www.solsticestudio.ai/datasets — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.

Procurement catalog:

• SolsticeAI Data Storefront — available via Datarade / Monda.

Citation

@dataset{solstice_arc_t_defi_pack_2026,
  title        = {ARC-T DeFi Decision Telemetry Pack (Sample)},
  author       = {SolsticeAI},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/solsticestudioai/arc-t-defi-pack}
}