Adding an external price feed

This tutorial builds upon the basis of the codebase in Streaming Data so ensure you have run through that tutorial first if you want to build a working bot.

Build with care

As described in the open source license, the Vega Protocol software and supporting documentation is provided “as is”, at your own risk, and without warranties of any kind.

The information provided in this tutorial does not constitute investment advice, financial advice, trading advice, or any other sort of advice and you should not treat any of the content as such. Gobalsky Labs Limited does not recommend that any asset should be bought, sold, or held by you. Do conduct your own due diligence and consult your financial advisor before making any investment decisions.

No developer or entity involved in creating the Vega protocol or supporting documentation will be liable for any claims or damages whatsoever associated with your use, inability to use, or your interaction with other users of the Vega Protocol, including any direct, indirect, incidental, special, exemplary, punitive or consequential damages, or legal costs, loss of profits, cryptocurrencies, tokens, or anything else of value.

Current setup

In the last tutorial we built a bot which listened to streams of market data to update it's knowledge of position and order state to ensure it could trade with full knowledge without having to make repeated queries and incur that time cost. Now that we have a working bot, in this tutorial we will add in a connection to a Binance price feed to make our quoting slightly smarter. Before we do that, let's review how we currently set our prices:

if position < max_abs_position:
    submissions.append(
        sub.OrderSubmission(
            market_id=market_id,
            size=1,
            price=latest_data.best_bid_price,
            time_in_force="TIME_IN_FORCE_GTC",
            type="TYPE_LIMIT",
            side="SIDE_BUY",
        )
    )
if position > -1 * max_abs_position:
    submissions.append(
        sub.OrderSubmission(
            market_id=market_id,
            size=1,
            price=latest_data.best_offer_price,
            time_in_force="TIME_IN_FORCE_GTC",
            type="TYPE_LIMIT",
            side="SIDE_SELL",
        )
    )

As you can see, the bot is pretty blindly quoting at whatever the best bid and offer are on the market. That works, but it's not exactly ideal. For one thing, if the market is quoting very incorrect prices they might not be useful, and secondly the bot could even be looking at your own orders, and if the rest of the market widens out those orders will just sit there until someone trades with them!

The next section offers one possible way to improve that, at least a little, and think about trading fees along the way.

Moving to external price

To do this, we're going to add in a feed to a Binance spot price, and then quote based on a spread around that. To start this off, add a new line to our requirements.txt for python-binance and run python -m pip install -r requirements.txt once again.

Now, create a new file in bot/ called binance_store.py and populate it with:

from threading import Lock
from typing import Any, Optional

from binance import ThreadedWebsocketManager
from binance.client import Client

from bot.models import ReferencePrice


class BinanceStore:
    def __init__(self, symbols_to_subscribe: list[str]):
        """Runs a websocket client listening to a list of binance tickers
        and storing their latest best bid/ask prices to be consumed later.

        Start the client by calling `start`.

        Args:
            symbols_to_subscribe:
                list[str], list of binance symbols to which to listen
        """
        self._ws_client = ThreadedWebsocketManager()
        self._client = Client()

        self._symbols = symbols_to_subscribe
        self._lock = Lock()

        # Elements in a list to allow atomic updates and avoid locks
        self._reference_prices = {}

    def start(self) -> None:
        """Start the websocket client, listening to passed symbols
        and storing their market data on each tick to be read on demand
        by trader.
        """
        for symb in self._symbols:
            ticker = self._client.get_orderbook_ticker(symbol=symb)
            self._reference_prices[ticker["symbol"]] = ReferencePrice(
                symbol=ticker["symbol"],
                bid_price=float(ticker["bidPrice"]),
                ask_price=float(ticker["askPrice"]),
            )

        self._ws_client.start()
        self._ws_client.start_multiplex_socket(
            callback=self._on_tick,
            streams=[f"{symb.lower()}@bookTicker" for symb in self._symbols],
        )

    def stop(self) -> None:
        """Stops the websocket client"""
        self._ws_client.stop()

    def _on_tick(self, tick: dict[str, Any]) -> None:
        tick_data = tick["data"]
        ref_price = ReferencePrice(
            symbol=tick_data["s"],
            bid_price=float(tick_data["b"]),
            ask_price=float(tick_data["a"]),
        )
        with self._lock:
            self._reference_prices[ref_price.symbol] = ref_price

    def get_reference_prices(self) -> list[ReferencePrice]:
        with self._lock:
            return list(self._reference_prices.values())

    def get_reference_price_by_symbol(self, symbol: str) -> Optional[ReferencePrice]:
        with self._lock:
            return self._reference_prices[symbol]

This is mostly boilerplate for interacting with the binance wrapper, but what we're broadly doing here is setting up a manager which will, like our VegaStore, listen to Binance websockets for a given price and update it's internal pricing. This will allow us to query that value each loop without having to wait for a REST request.

Next, add a new section to .env:

# Binance config
BINANCE_MARKET=YOUR_MARKET_HERE

This sets up our Binance connection parameters. Choose a Binance spot market name which is a good match for your chosen Vega market and put its name in BINANCE_MARKET field. For example if you are looking at a BTC-USDC market on Vega you might put in BTCUSDC.

Now jump back to main.py and make a few changes. First, add an import for our new class at the top:

from bot.binance_store import BinanceStore

Connecting the feed

Now update the _run function to have these arguments (adding in binance_store):

def _run(
    node_rest_url: str,
    market_id: str,
    party_id: str,
    token: str,
    wallet_url: str,
    vega_store: store.VegaStore,
    binance_store: BinanceStore,
    binance_symbol: str,
    max_abs_position=1,
):

Now to create it and pipe it in there, update the main function to start with:

def main(
    node_rest_url: str,
    node_ws_url: str,
    market_id: str,
    party_id: str,
    token: str,
    wallet_url: str,
    binance_market: str,
    max_abs_position=1,
):
    vega_store = store.VegaStore(websocket_url=node_ws_url, rest_api_url=node_rest_url)
    vega_store.start(market_id=market_id, party_id=party_id)

    binance_store = BinanceStore(symbols_to_subscribe=[binance_market])
    binance_store.start()

    run_thread = threading.Thread(
        target=_run,
        kwargs={
            "node_rest_url": node_rest_url,
            "market_id": market_id,
            "party_id": party_id,
            "token": token,
            "wallet_url": wallet_url,
            "max_abs_position": max_abs_position,
            "vega_store": vega_store,
            "binance_store": binance_store,
            "binance_symbol": binance_market,
        },
        daemon=True,
    )
    run_thread.start()

    # Now run event loop (Send SIGINT (Ctrl+C) to close)
    rel.dispatch()
    vega_store.stop()
    binance_store.stop()  # Adding a stop here for the Binance store too

Don't forget to retain the section cancelling the liquidity commitment if you have already run through that tutorial and finally pass in the required market by updating our call to main to be:

main(
    node_rest_url=os.environ["NODE_URL"],
    node_ws_url=os.environ["WS_URL"],
    market_id=os.environ["MARKET_ID"],
    token=os.environ["WALLET_TOKEN"],
    party_id=os.environ["PARTY_ID"],
    wallet_url=os.environ["WALLET_URL"],
    binance_market=os.environ["BINANCE_MARKET"],
    max_abs_position=1,
)

Incorporating fees

Now that you have access to the Binance store inside the bot, you can make use of it.

But first a short digression on fees, because if you're setting the prices, it is worth considering them. By taking a price feed from Binance, what we are really considering is that if there is a difference between a price on Binance and on the corresponding Vega market, the bot may want to capitalise on that difference by buying/selling on Vega and selling/buying on Binance. However, to do that you're probably going to incur fees, (though these will vary depending on how the trades are executed) so you only want to do this if the difference is large enough to profit.

To take some indicative numbers, and make some assumptions, at time of writing both the maker and taker fees for spot trading on Binance sit at 0.1% assuming no discounts are applied. Meanwhile the maker fee, paid to the maker in a trade on the Vega Fairground testnet is 0.02%. It is entirely possible both of these numbers have changed by the time you read this so be sure to explore for yourself, and fees on a given network are dependent on community decisions, but for now they will do. If you combine the 0.1% fee from Binance and a 0.02% rebate from Vega Protocol you probably only want to execute if you get a price 0.08% better than on Binance - e.g. sell 0.08% higher than you can buy on Binance and buy 0.08% lower than you can sell. For now, for simplicity, let's assume on Binance we execute with market orders to close out the trade immediately.

In a real strategy, you might also want to take into account:

• Fees to transfer funds between Binance and Vega Protocol, as they may build up in one location or another
• Execution differences. Perhaps you will sometimes want to aggresively take a price on Vega if it is very good, however this will incur higher fees so it may be worth considering if the difference is large enough

For now though to implement the strategy, update price generation to:

ref_price = binance_store.get_reference_price_by_symbol(binance_symbol)
if position < max_abs_position:
    submissions.append(
        sub.OrderSubmission(
            market_id=market_id,
            size=1,
            price=ref_price.bid_price * 0.992,
            time_in_force="TIME_IN_FORCE_GTC",
            type="TYPE_LIMIT",
            side="SIDE_BUY",
        )
    )
if position > -1 * max_abs_position:
    submissions.append(
        sub.OrderSubmission(
            market_id=market_id,
            size=1,
            price=ref_price.bid_price * 1.008,
            time_in_force="TIME_IN_FORCE_GTC",
            type="TYPE_LIMIT",
            side="SIDE_SELL",
        )
    )

You can see here it's taking the reference price from Binance, looking at the current bid price, which it would be trying to execute at on Binance if someone buys on Vega, and scaling that out by the fees incurred. We then do similar for the sale price. In the real world, this transaction would make no profit, but it should at least help with liquidity on the market.

Putting it together

From here, you can kick off your bot once more with python -m main and see the results. You will likely see that your spreads have become much wider as a result, and may no longer be competitive, which is fair because if it were this easy someone else would probably have already done it! However we've now covered the basics of how this setup could work, and how to listen to an external price and feed that through, considering fees, into a potential price.

If you want to look at another way to potentially enhance your bot, and haven't already run through it, jump over to the liquidity provision tutorial to learn about maintaining a liquidity commitment.