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license: mit |
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task_categories: |
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- time-series-forecasting |
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- tabular-classification |
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tags: |
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- finance |
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- defi |
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- amm |
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- ethereum |
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- cryptocurrency |
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- transaction |
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- microstructure |
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pretty_name: AMM-Events (Event-Aware DeFi Dataset) |
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size_categories: |
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- 100M<n<1B |
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--- |
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# AMM-Events: A Multi-Protocol DeFi Event Dataset |
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## Dataset Description |
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**AMM-Events** is a high-fidelity, block-level dataset capturing **8.9 million on-chain events** from the Ethereum mainnet, specifically designed for event-aware forecasting and market microstructure analysis in Decentralized Finance (DeFi). |
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Unlike traditional financial datasets based on Limit Order Books (LOB), this dataset focuses on **Automated Market Makers (AMMs)**, where price dynamics are triggered exclusively by discrete on-chain events (e.g., swaps, mints, burns) rather than continuous off-chain information. |
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- **Paper Title:** Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols |
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- **Total Events:** 8,917,353 |
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- **Time Span:** Jan 1, 2024 – Sep 16, 2025 |
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- **Block Range:** 18,908,896 – 23,374,292 |
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- **Protocols:** Uniswap V3, Aave, Morpho, Pendle |
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- **Granularity:** Block-level timestamps & transaction-level event types |
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### Supported Tasks |
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- **Event Forecasting:** Predicting the next event type (classification/TPP) and time-to-next-event (regression/TPP). |
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- **Market Microstructure Analysis:** Analyzing causal synchronization between liquidity events and price shocks. |
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- **Anomaly Detection:** Identifying "Black Swan" traffic surges or congestion events. |
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--- |
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## Dataset Structure |
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The data is organized into a standardized JSON format. Each entry decouples complex smart contract logic into interpretable metrics. |
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### Data Fields |
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- `block_number` (int): The Ethereum block height where the event occurred. |
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- `timestamp` (int): Unix timestamp of the block. |
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- `transaction_hash` (string): Unique identifier for the transaction. |
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- `protocol` (string): Origin protocol (`Uniswap V3`, `Aave`, `Morpho`, or `Pendle`). |
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- `event_type` (string): The category of the event (`Swap`, `Mint`, `Burn`, `UpdateImpliedRate`, etc.). |
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- `payload` (dict): Protocol-specific metrics (e.g., `amount0`, `amount1`, `liquidity`, `tick` for Uniswap). |
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### Data Splits |
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The dataset covers **359 liquidity pools** selected for high activity and representativeness: |
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- **Pendle:** 296 pools (Yield Trading) |
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- **Aave:** 53 pools (Lending) |
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- **Uniswap V3:** 5 pools (Spot Trading) |
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- **Morpho:** 5 pools (Lending Optimization) |
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--- |
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## Usage |
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### Loading the Data |
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You can load this dataset directly using the Hugging Face `datasets` library: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("Jackson668/AMM-Events") |
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# Example: Accessing the first train example |
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print(dataset['train']) |
