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Solvers and aggregators often need to estimate what price they’d get for a specific trade size before committing to a firm RFQ quote. The Price API stream provides enough depth data to calculate a Volume-Weighted Average Price (VWAP) locally, giving you a reliable indicative price at any size.
Use case: You’re a solver or aggregator evaluating whether to bid on an intent. Instead of requesting a firm quote (which has rate limits and expiry), you estimate the execution price from the live stream to decide if the trade is worth pursuing.

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How It Works

The Price API streams order book levels as (price, size) pairs, sorted best-first. To estimate the execution price for a target trade size, you walk through these levels from best to worst, accumulating volume until you’ve filled the target amount. The VWAP is the notional-weighted average price across all levels you’d consume.

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The Algorithm

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Step-by-Step Breakdown

1

Sort levels by best price

For a buy, sort asks lowest-first (cheapest prices first). For a sell, sort bids highest-first (best bid prices first).
2

Walk through levels

For each level, calculate the notional value (price × size). Take the lesser of the level’s notional and your remaining target - this handles partial fills on the last level.
3

Accumulate

Track total base tokens filled and total quote spent. The ratio gives you the VWAP.
4

Check for sufficient liquidity

If remaining > 0 after exhausting all levels, the stream doesn’t have enough depth for your size. You may want to fall back to a firm quote or split across sources.
def estimate_vwap(
    levels: list[tuple[float, float]],
    target_notional: float,
    intent: str,  # "buy" or "sell"
) -> tuple[float, float, float]:
    """
    Estimate the VWAP for a target notional trade size.

    Args:
        levels: Price levels as (price, size) tuples from the stream.
                Bids are highest-first, asks are lowest-first.
        target_notional: The total notional (in quote terms) you want to trade.
        intent: "buy" (you're buying base, consuming asks)
                or "sell" (you're selling base, consuming bids).

    Returns:
        (vwap, unfilled)
        - vwap: the effective execution price
        - unfilled: remaining notional that couldn't be filled (0 if fully filled)
    """
    # Sort: cheapest first for buys, most expensive first for sells
    sorted_levels = sorted(levels, key=lambda l: l[0], reverse=(intent == "sell"))

    remaining = target_notional
    total_base = 0.0
    total_quote = 0.0

    for price, size in sorted_levels:
        if remaining <= 0:
            break

        level_notional = price * size
        fill_notional = min(level_notional, remaining)
        fill_base = fill_notional / price

        total_quote += fill_notional
        total_base += fill_base
        remaining -= fill_notional

    if total_base == 0:
        return 0.0, target_notional

    vwap = total_quote / total_base
    return vwap, remaining

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Full Example

Combining the VWAP estimation with the Price API stream from the Quickstart: 
import asyncio

import httpx
import websockets

from bebop_pb2 import BebopPricingUpdate  # type: ignore

NETWORK = "ethereum"
USERNAME = "<you_username>"
API_KEY = "<your_api_key>"

WSS_URL = (
    f"wss://api.bebop.xyz/pmm/{NETWORK}/v3/pricing"
    f"?format=protobuf"
    f"&name={USERNAME}"
    f"&authorization={API_KEY}"
    f"&gasless=false"
    f"&expiry_type=standard"
)

PAIR = "WETH/USDC"
TARGET_NOTIONAL = 100_000  # estimate price for $100k trade

def address_to_hex(b: bytes) -> str:
    return "0x" + b.hex()

def to_levels(flat: list[float]) -> list[tuple[float, float]]:
    it = iter(flat)
    return list(zip(it, it, strict=True))

def estimate_vwap(
    levels: list[tuple[float, float]], target_notional: float, intent: str
) -> tuple[float, float]:
    sorted_levels = sorted(levels, key=lambda lv: lv[0], reverse=(intent == "sell"))

    remaining = target_notional
    total_base = 0.0
    total_quote = 0.0

    for price, size in sorted_levels:
        if remaining <= 0:
            break

        level_notional = price * size
        fill_notional = min(level_notional, remaining)
        fill_base = fill_notional / price

        total_quote += fill_notional
        total_base += fill_base
        remaining -= fill_notional

    if total_base == 0:
        return 0.0, target_notional

    return total_quote / total_base, remaining

# Resolve token addresses
resp = httpx.get(f"https://api.bebop.xyz/pmm/{NETWORK}/v3/tokenlist", timeout=10.0)
tokens = {t["symbol"]: t for t in resp.json().get("tokens", {})}
base_symbol, quote_symbol = PAIR.split("/")
base_addr = tokens[base_symbol]["address"].lower()
quote_addr = tokens[quote_symbol]["address"].lower()

async def main():
    async with websockets.connect(
        WSS_URL, ping_interval=20, ping_timeout=10, max_size=2**21
    ) as ws:
        print(f"Connected - estimating VWAP for {PAIR} at ${TARGET_NOTIONAL:,}\n")

        async for blob in ws:
            update = BebopPricingUpdate()
            update.ParseFromString(blob)

            for pair in update.pairs:
                if (
                    address_to_hex(pair.base).lower() != base_addr
                    or address_to_hex(pair.quote).lower() != quote_addr
                ):
                    continue

                bids = to_levels(list(pair.bids))
                asks = to_levels(list(pair.asks))

                if not bids or not asks:
                    continue

                # Estimate VWAP for buying $100k of WETH
                buy_vwap, buy_unfilled = estimate_vwap(asks, TARGET_NOTIONAL, "buy")

                # Estimate VWAP for selling $100k of WETH
                sell_vwap, sell_unfilled = estimate_vwap(bids, TARGET_NOTIONAL, "sell")

                print(
                    f"  BUY  ${TARGET_NOTIONAL:>8,}  "
                    f"vwap: {buy_vwap:.2f}"
                    f'{"  ⚠ unfilled: " + f"${buy_unfilled:,.0f}" if buy_unfilled > 0 else ""}\n'
                    f"  SELL ${TARGET_NOTIONAL:>8,}  "
                    f"vwap: {sell_vwap:.2f}"
                    f'{"  ⚠ unfilled: " + f"${sell_unfilled:,.0f}" if sell_unfilled > 0 else ""}\n'
                )
asyncio.run(main())
Example output for a $100,000 WETH/USDC estimate: 
{
  "pair": "WETH/USDC",
  "target_notional": 100000,
  "buy": {
    "vwap": 2330.0,
    "unfilled": 0.0
  },
  "sell": {
    "vwap": 2328.39,
    "unfilled": 0.0
  }
}
These are indicative estimates based on streamed depth. The actual execution price from a firm quote may differ due to market maker inventory changes, timing, and quote-specific parameters.

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Key Considerations

  • Check for sufficient depth. If unfilled > 0 after exhausting all levels, the stream doesn’t have enough liquidity for your trade size. Fall back to a firm quote or split across sources.
  • VWAP diverges from top-of-book at size. For small trades the top level is a reasonable proxy. For larger sizes, the VWAP will be meaningfully worse - that’s the whole point of estimating it.
  • Tier cadence varies between pairs. Two pairs with the same top-of-book price can have very different depth profiles. Always estimate at your actual trade size rather than assuming uniform depth.
  • Stream prices update frequently. Re-estimate on each new message rather than caching stale VWAP values.