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

Biswap is a non-custodial exchange protocol built on BNB chain. Biswap was founded by an anonymous team and launched in 2021.

BSW tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). Biswap’s investors include Binance Labs.

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Biswap

# Biswap Average trading volume (per user) Methodology

## 1. Overview

The trading volume average per user metric measures the average USD trading volume generated per daily active user on Biswap. This metric provides insight into user trading intensity by dividing total daily trading volume by the number of unique daily active users. The calculation combines data from both Biswap v1 and v3 protocols on Binance Smart Chain (BSC), applying price manipulation filters and using HyperLogLog aggregation for scalable user counting.

## 2. Data sources

- **Blockchain events**: Parsed BSC `Pair_Swap` events from Biswap v1 and `Pool_Swap` events from Biswap v3 containing swap amounts, token addresses, and transaction details
- **Transaction data**: BSC transaction records providing user addresses for each swap transaction
- **Function call traces**: BSC traces for Biswap v3 `BiswapPoolV3_swapY2X_function` and `BiswapPoolV3_swapX2Y_function` calls to determine swap directions
- **Factory events**: Parsed `Factory_PairCreated_event` logs from Biswap v1 and `Factory_NewPool_event` logs from Biswap v3 defining trading pairs and fee structures
- **External data**: End-of-day token price feeds for USD conversion and token metadata including decimals and symbols for amount normalization

## 3. Step-by-step calculation

### Component A: Trade data extraction
- **Purpose**: Extract all swap transactions from Biswap protocols with user identification
- **Logic**: Collect swap events from both Biswap v1 `Pair_Swap` events and v3 `Pool_Swap` events on BSC. For v1 swaps, calculate token amount changes by subtracting `amount0Out` from `amount0In` and `amount1Out` from `amount1In`. For v3 swaps, use `amountX` and `amountY` from return values, applying directional multipliers determined by matching with function call traces (`swapY2X` uses -1 for directionX and 1 for directionY, while `swapX2Y` uses 1 for directionX and -1 for directionY). Join with transaction records to obtain user addresses from the `from_address` field. Filter for successful transactions only using `status = true` condition.
- **Key fields**: `block_timestamp`, `transaction_hash`, `user_address`, `token0_amount`, `token1_amount`, `account_address`

### Component B: Trading pair metadata enrichment
- **Purpose**: Add token metadata and fee information to enable proper volume calculations
- **Logic**: Join trade data with trading pair dimensions built from factory events. For v1 pairs, use `Factory_PairCreated_event` with hardcoded fee tiers (0.2% standard, 0.1% for specific stable pools). For v3 pairs, use `Factory_NewPool_event` with dynamic fee tiers from the `fee` field divided by 1,000,000. Enrich with token metadata to get decimal places and symbols for both tokens in each pair. Create trading pair names by concatenating token symbols.
- **Key fields**: `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `fee_tier`, `account_name`

### Component C: Volume calculation and USD conversion
- **Purpose**: Convert raw token amounts to normalized USD trading volumes with manipulation filtering
- **Logic**: Correct token amounts by dividing absolute values by 10 raised to the respective token's decimal places. Join with end-of-day price feeds to get USD prices for both tokens on the transaction date. Calculate USD values for both tokens by multiplying corrected amounts with respective prices. Apply price manipulation filter by taking the lesser USD value between the two tokens using the `least()` function, defaulting to zero if either price is unavailable. Aggregate volumes by day, transaction, and log index to avoid double counting.
- **Key fields**: `trading_volume` (calculated as least USD value), `timestamp` (daily truncated), `chain`, `version`

### Component D: Daily active user counting
- **Purpose**: Count unique daily active users efficiently using probabilistic counting
- **Logic**: Extract unique user addresses from all trade transactions on a daily basis, categorizing them as 'Trader' user type. Use HyperLogLog (HLL) initialization and aggregation functions for scalable distinct counting across multiple dimensional breakdowns including project-level, chain-level, user-type-level, and product-level aggregations. The final calculation uses the 'project_dbt_tag' dimension which provides total unique users across all Biswap products and chains.
- **Key fields**: `user_dau` (HLL count), `dimensions`, `timestamp_grain` (day), `user_type`

### Final aggregation
- **Combining components**: Join daily trading volume totals with daily active user counts on matching timestamps, using the 'project_dbt_tag' dimension for user counts to get total unique users across all Biswap activities
- **Currency conversion**: USD conversion occurs at the individual trade level using end-of-day price feeds, with volumes pre-aggregated in USD before the final division
- **Time aggregation**: Both components use daily timestamp truncation with 'day' granularity for consistent temporal alignment
- **Final calculation**: Divide total daily trading volume by daily active user count using safe division to handle scenarios with zero active users, resulting in average trading volume per user in USD

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

Average trading volume (per user)

Trading volume divided by daily active users.

BiswapBSW

Average trading volume (per user)

Average trading volume (per user)

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