Trading and Orders
Crypto Trades
Introduction
All fiat and Crypto currencies are individual assets. When you buy a pair like BTCUSDT, you trade USDT for BTC. In this case, LEAN removes some USDT from your portfolio cash book and adds some BTC. The virtual pair BTCUSDT represents your position in the trade, but the virtual pair doesn't actually exist. It simply represents an open trade.
Place Trades
When you place Crypto trades, don't use the calculate_order_quantity
or set_holdings
methods. Instead, calculate the order quantity based on the currency amounts in your cash book and place manual orders.
The following code snippet demonstrates how to allocate 90% of your portfolio to BTC.
def on_data(self, data: Slice): self.set_crypto_holdings(self._symbol, .9) def set_crypto_holdings(self, symbol, percentage): crypto = self.securities[symbol] base_currency = crypto.base_currency # Calculate the target quantity in the base currency target_quantity = percentage * (self.portfolio.total_portfolio_value - self.settings.free_portfolio_value) / base_currency.conversion_rate quantity = target_quantity - base_currency.amount # Round down to observe the lot size lot_size = crypto.symbol_properties.lot_size quantity = round(quantity / lot_size) * lot_size if self.is_valid_order_size(crypto, quantity): self.market_order(symbol, quantity) # Brokerages have different order size rules # Binance considers the minimum volume (price x quantity): def is_valid_order_size(self, crypto, quantity): return abs(crypto.price * quantity) > crypto.symbol_properties.minimum_order_size
The preceding example doesn't take into account order fees. You can add a 0.1% buffer to accommodate it.
The following example demonstrates how to form an equal-weighted Crypto portfolio and stay within the cash buffer.
def on_data(self, data: Slice): percentage = (1 - self.settings.free_portfolio_value_percentage) / len(self.symbols); for symbol in self.symbols: self.set_crypto_holdings(symbol, percentage)
You can replace the settings.free_portfolio_value_percentage
for a class variable (e.g. self.cash_buffer
).
When you place Crypto trades, ensure you have a sufficient balance of the base or quote currency before each trade. If you hold multiple assets and you want to put all of your capital into BTCUSDT, you need to first convert all your non-BTC assets into USDT and then purchase BTCUSDT.
By default, the account currency is USD and your starting cash is $100,000. Some Crypto exchanges don't support fiat currencies, which means you can't convert the $100,000 USD to the pair's quote currency. In this case, set your account currency to the quote currency with a positive quantity.
self.set_account_currency("USDT", 1000) # Set the account currency to Tether and its quantity to 1,000 USDT
For a full example of placing crypto trades, see the BasicTemplateCryptoAlgorithm .
Liquidate Positions
If you use the liquidate
method to liquidate a Crypto position, it only liquidates the quantity of the virtual pair. Since the virtual pair BTCUSDT may not represent all of your BTC holdings, don't use the liquidate
method to liquidate Crypto positions. Instead, calculate the order quantity based on the currency amounts in your cash book and place manual orders. The following code snippet demonstrates how to liquidate a BTCUSDT position.
def on_data(self, data: Slice): self.liquidate_crypto(self._symbol) def liquidate_crypto(self, symbol): crypto = self.securities[symbol] base_currency = crypto.base_currency # Avoid negative amount after liquidate quantity = min(crypto.holdings.quantity, base_currency.amount) # Round down to observe the lot size lot_size = crypto.symbol_properties.lot_size; quantity = (round(quantity / lot_size) - 1) * lot_size if self.is_valid_order_size(crypto, quantity): self.market_order(symbol, -quantity)
The order fees don't respect the lot size. When you try to liquidate a position, the absolute value of the base currency quantity can be less than the lot size and greater than zero.
In this case, your algorithm holds a position that you can't liquidate, yet self.portfolio[symbol].invested
is False
.
For more information about self.portfolio[symbol].invested
, see Holdings Status.
The following code snippet demonstrates how to determine if you can liquidate a position:
def on_data(self, data: Slice): crypto = self.securities[self._symbol] if abs(crypto.holdings.quantity) > crypto.symbol_properties.lot_size: self.liquidate_crypto(self._symbol)
Examples
The following examples demonstrate some common practices for crypto holdings.
Example 1: ETH-BTC Proxy Trading
The following algorithm trades trend-following of the ETHBTC crypto pair using a 20-day EMA indicator. To reduce friction (e.g. slippage), we trade the more liquid and popular BTCUSDT and ETHUSDT pair instead. For example, if ETHBTC is above EMA (uptrend), we buy ETHUSDT and sell BTCUSDT with the same size in USDT.
class CryptoHoldingsAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2024, 1, 1) self.set_end_date(2024, 3, 1) self.set_account_currency("USDT", 1000000) # Seed the last price to set the initial holding price of the cryptos. self.set_security_initializer(BrokerageModelSecurityInitializer(self.brokerage_model, FuncSecuritySeeder(self.get_last_known_prices))) # We would like to trade the EMA cross between 2 popular cryptos: BTC & ETH, # so we request ETHBTC data to find trading opportunities. self.ethbtc = self.add_crypto("ETHBTC", Resolution.DAILY, market=Market.COINBASE).symbol # Trade through BTCUSDT & ETHUSDT, though, since stable coin trades have lower friction costs and higher liquidity. btcusdt = self.add_crypto("BTCUSDT", Resolution.DAILY, market=Market.COINBASE) ethusdt = self.add_crypto("ETHUSDT", Resolution.DAILY, market=Market.COINBASE) self.btcusdt = btcusdt.symbol self.ethusdt = ethusdt.symbol # Simulate an account with various crypto cash through holdings. btcusdt.holdings.set_holdings(btcusdt.price, 5) ethusdt.holdings.set_holdings(ethusdt.price, 22.5) # Add automatic updating of the EMA indicator for trend trade signal emission. self._ema = self.ema(self.ethbtc, 20, Resolution.DAILY) # Warm up the indicator for its readiness usage immediately. self.warm_up_indicator(self.ethbtc, self._ema, Resolution.DAILY) def on_data(self, slice: Slice) -> None: bar = slice.bars.get(self.ethbtc) btc = slice.bars.get(self.btcusdt) eth = slice.bars.get(self.ethusdt) if bar and self._ema.is_ready and btc and eth: ema = self._ema.current.value # ETHBTC's current price is higher than EMA, suggesting an uptrend. if bar.close > ema and not self.portfolio[self.btcusdt].is_short: # Calculate the order size needed to have equal BTC-ETH value exposure. btc_size, eth_size = self.calculate_order_size(btc.close, eth.close) # To follow the up trend of ETHBTC, sell BTCUSDT and buy ETHUSDT. self.market_order(self.btcusdt, -btc_size) self.market_order(self.ethusdt, eth_size) # ETHBTC's current price is below the EMA, suggesting a downtrend. elif bar.close < ema and not self.portfolio[self.btcusdt].is_long: # Calculate the order size needed to have equal BTC-ETH value exposure. btc_size, eth_size = self.calculate_order_size(btc.close, eth.close) # To follow the downtrend of ETHBTC, buy BTCUSDT and sell ETHUSDT. self.market_order(self.ethusdt, -eth_size) self.market_order(self.btcusdt, btc_size) def calculate_order_size(self, btc_price: float, eth_price: float) -> Tuple[float]: # Invest 10% of the portfolio in the 2-leg trade, which will be 5% per each leg. position_value = self.portfolio.total_portfolio_value * 0.05 # Calculate the extra position needed to hold BTC/ETH in the same size as USDT in the 2-leg trade. btc_size = (position_value - self.portfolio.cash_book["BTC"].value_in_account_currency) / btc_price eth_size = (position_value - self.portfolio.cash_book["ETH"].value_in_account_currency) / eth_price return btc_size, eth_size