Liquidity providers deposit funds into SpookySwap and receive trading fees from traders (supply-side fees).

SpookySwap is a non-custodial exchange protocol built on Fantom. SpookySwap was launched in April 2021.

BOO tokenholders own and govern the protocol. They currently take a cut of the total interest paid by borrowers (revenue).

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SpookySwap

# SpookySwap Trade count Methodology

## 1. Overview

Trade count measures the number of unique trading transactions executed on SpookySwap on a daily basis. This metric counts distinct transaction hashes containing swap activity, providing insight into trading frequency and user engagement with the decentralized exchange. The calculation processes raw swap events from SpookySwap V1 contracts on Fantom, applies data quality filters, and aggregates unique transactions by day.

## 2. Data sources

- **Blockchain events**: Parsed Fantom `Pair_Swap_event` logs from SpookySwap V1 pair contracts containing swap activity data including token amounts and user addresses
- **Contract events**: Fantom `PairFactory_PairCreated_event` logs that establish valid trading pairs with token addresses and fee tier information
- **Transaction data**: Fantom transaction records providing user address identification and transaction metadata
- **External data**: End-of-day token price feeds for USD conversion and price manipulation filtering
- **Token metadata**: Token information including decimal places and symbols for proper amount calculation and pair naming

## 3. Step-by-step calculation

### Component A: Raw trade extraction
- **Purpose**: Extract individual swap transactions from SpookySwap V1 contracts
- **Logic**: Parse all `Pair_Swap_event` logs from SpookySwap V1 pair contracts on Fantom blockchain starting from April 17, 2021. For each swap event, calculate the net token amount changes by subtracting `amount0Out` from `amount0In` for token0 and `amount1Out` from `amount1In` for token1. Join with Fantom transaction records to identify the actual user address (from `from_address`) versus the contract user address (`sender` from the swap event). Filter data to include only transactions from the launch date forward.
- **Key fields**: `transaction_hash` for trade identification, `log_index` for event ordering, `amount0In/Out` and `amount1In/Out` for calculating token changes, `sender` for contract interaction address, `block_timestamp` for trade timing

### Component B: Trading pair dimensions
- **Purpose**: Establish valid trading pairs with metadata for trade classification
- **Logic**: Process `PairFactory_PairCreated_event` logs to identify all valid SpookySwap V1 trading pairs. Extract the pair contract address, token0 and token1 addresses, and apply a fixed 0.2% fee tier for all V1 pairs. Enrich with token metadata including decimal places and symbols. Create descriptive pair names by concatenating token symbols with a hyphen separator.
- **Key fields**: `pair` contract address for trade matching, `token0` and `token1` addresses for token identification, token `decimals` for amount correction, token `symbol` for pair naming

### Component C: Trade enrichment and volume calculation
- **Purpose**: Combine raw trades with pair metadata and calculate USD-adjusted trading volumes
- **Logic**: Join swap events with trading pair dimensions using pair contract address and chain. Calculate absolute token amounts by dividing raw token changes by 10 raised to the token's decimal places. Aggregate trades at the daily level while preserving transaction-level granularity for counting. Convert token amounts to USD using end-of-day price feeds matched by timestamp, chain, and token address. Apply price manipulation filtering by taking the lesser USD value between token0 and token1 amounts, with fallback to zero if prices are unavailable.
- **Key fields**: `token0_amount` and `token1_amount` after decimal correction, USD conversion using `price_usd` from price feeds, `timestamp` truncated to day boundary

### Component D: Daily trade counting
- **Purpose**: Count unique trading transactions per day with proper dimensional grouping
- **Logic**: From the enriched trading volume data, count distinct transaction hashes grouped by date, chain, market sector (`exchanges`), business segment (`dex`), and product dimensions. Each unique transaction hash represents one trade regardless of how many swap events it contains. Preserve all dimensional attributes including chain ID, business classifications, and temporal granularity.
- **Key fields**: `trade_count` as count of distinct `transaction_hash` values, `timestamp` for daily boundaries, dimensional grouping fields for proper aggregation

### Final aggregation
- **Combining components**: The final metric aggregates unique transaction hashes from the trading volumes data, ensuring each transaction is counted only once per day regardless of multiple swap events within the same transaction
- **Currency conversion**: USD conversion occurs at the volume calculation stage using end-of-day price feeds, with price manipulation filtering applied before the trade counting step
- **Time aggregation**: Daily boundaries are established by truncating timestamps to day level, with all trades within a 24-hour period counted together
- **Final calculation**: The trade count equals the number of distinct transaction hashes containing SpookySwap swap activity per day, processed through the `finalize_metric_trade_count` macro with zero-filling strategy and chain availability filtering

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Number of trades executed on a decentralized exchange.

SpookySwapBOO

Trade count

Trade count

Competitive landscape

Chain breakdown