Universes
Crypto
Crypto Universes
To add a universe of Cryptocurrencies, in the initialize
method, pass a CryptoUniverse
to the add_universe
method.
def initialize(self) -> None: self.universe_settings.asynchronous = True self.universe_settings.resolution = Resolution.DAILY self.set_brokerage_model(BrokerageName.COINBASE, AccountType.CASH) # Add crypto universe selection self._universe = self.add_universe(CryptoUniverse.coinbase(lambda universe_day: [c.symbol for c in universe_day]))
The following table shows the helper methods to create Crypto universes for the supported exchanges:
Brokerage Name | Helper Method | Example |
---|---|---|
BrokerageName. BINANCE | CryptoUniverse. binance | Example |
BrokerageName. BINANCE_US | CryptoUniverse. binance_us | Example |
BrokerageName. BITFINEX | CryptoUniverse. bitfinex | Example |
BrokerageName. BYBIT | CryptoUniverse. bybit | Example |
BrokerageName. COINBASE | CryptoUniverse. coinbase | Example |
BrokerageName. KRAKEN | CryptoUniverse. kraken | Example |
The following table describes the CryptoUniverse
method arguments:
Argument | Data Type | Description | Default Value |
---|---|---|---|
selector | Callable[[List[CryptoUniverse]], List[Symbol]] | A function to select some of the Cryptocurrencies for the universe. | |
universe_settings | UniverseSettings | The universe settings. | None |
The filter function receives CryptoUniverse
objects, which represent one of the Cryptocurrencies in the market. The Symbol
objects that the filter function returns represent the universe constituents. CryptoUniverse
objects have the following attributes:
To perform thorough filtering on the CryptoUniverse
objects, define an isolated filter method.
def initialize(self) -> None: self.universe_settings.asynchronous = True self.universe_settings.resolution = Resolution.DAILY self.set_brokerage_model(BrokerageName.COINBASE, AccountType.CASH) # Add crypto universe selection self._universe = self.add_universe(CryptoUniverse.coinbase(self._universe_filter))
def _universe_filter(self, universe_day): # Define the universe selection function return [cf.symbol for cf in universe_day if cf.volume >= 100 and cf.volume_in_usd > 10000]
Historical Data
To get historical Cryptocurrency universe data, call the history
method with the Universe
object and the lookback period. The return type is a multi-index pandas.Series
or pandas.DataFrame
containing CryptoUniverse
data.
# Get 30 days of history for the Crypto universe. # DataFrame object: df_history = self.history(self._universe, 30, Resolution.DAILY, flatten=True) # Series object: series_history = self.history(self._universe, 30, Resolution.DAILY) for (univere_symbol, time), universe_day in history.items(): for universe_item in universe_day: self.log(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")
For more information about Cryptocurrency data, see Crypto.
Alternative Data Universes
An alternative data universe lets you select a basket of Cryptocurrencies based on an alternative dataset that's linked to them. If you use an alternative data universe, you limit your universe to only the securities in the dataset, which avoids unnecessary subscriptions. Currently, only the Crypto Market Cap alternative dataset supports universe selection for Crypto.
Selection Frequency
Crypto universes run on a daily basis by default. To adjust the selection schedule, see Schedule.
Examples
The following examples demonstrate some common Crypto universes.
Example 1: Highly Liquid Crypto Universe
To fairly compare the liquidity between Crypto pairs, use the
volume_in_usd
property of the
CryptoUniverse
objects during universe selection.
The following algorithm selects the 10 Crypto pairs with the greatest USD volume on Bitfinex:
class HighlyLiquidCryptoUniverseAlgorithm(QCAlgorithm): def initialize(self): self.set_start_date(2021, 1, 1) self.add_universe(CryptoUniverse.bitfinex(self._select_assets)) def _select_assets(self, data): selected = sorted( [x for x in data if x.volume_in_usd], key=lambda x: x.volume_in_usd )[-10:] return [x.symbol for x in selected]
Example 2: Highly Volatile Crypto Universe
The following algorithm creates a universe of volatile Crypto pairs on Binance by selecting the pairs with the largest trading range in the previous day:
class HighlyVolatileCryptoUniverseAlgorithm(QCAlgorithm): def initialize(self): self.set_start_date(2021, 1, 1) self.add_universe(CryptoUniverse.binance(self._select_assets)) def _select_assets(self, data): selected = sorted(data, key=lambda x: (x.high - x.low) / x.low)[-10:] return [x.symbol for x in selected]
Example 3: Large Cap Crypto Universe
The following algorithm uses the CoinGecko Crypto Market Cap dataset to create a universe of the largest Crypto pairs on Coinbase. To avoid trading errors, it only selects pairs that have a quote currency that matches your account currency . At noon each day, it rebalances the portfolio to give equal weight to each coin in the universe.
class LargeCapCryptoUniverseAlgorithm(QCAlgorithm): def initialize(self): self.set_start_date(2020, 1, 1) self.set_end_date(2021, 1, 1) # Set the account currency. USD is already the default value. Change it here if you want. self.set_account_currency("USD") # Get the pairs that our brokerage supports and have a quote currency that # matches your account currency. We need this list in the universe selection function. self._market = Market.COINBASE self._market_pairs = [ x.key.symbol for x in self.symbol_properties_database.get_symbol_properties_list(self._market) if x.value.quote_currency == self.account_currency ] # Add a universe of Cryptocurrencies. self._universe = self.add_universe(CoinGeckoUniverse, self._select_assets) # Add a Sheduled Event to rebalance the portfolio. self.schedule.on(self.date_rules.every_day(), self.time_rules.at(12, 0), self._rebalance) def _select_assets(self, data: List[CoinGeckoUniverse]) -> List[Symbol]: # Select the coins that our brokerage supports and have a quote currency that matches # our account currency. tradable_coins = [d for d in data if d.coin + self.account_currency in self._market_pairs] # Select the largest coins and create their Symbol objects. return [ c.create_symbol(self._market, self.account_currency) for c in sorted(tradable_coins, key=lambda x: x.market_cap)[-10:] ] def _rebalance(self): if not self._universe.selected: return symbols = [symbol for symbol in self._universe.selected if self.securities[symbol].price] # Liquidate coins that are no longer in the universe. targets = [PortfolioTarget(symbol, 0) for symbol, holding in self.portfolio.items() if holding.invested and symbol not in symbols] # Form an equal weighted portfolio of the coins in the universe. targets += [PortfolioTarget(symbol, 0.5/len(symbols)) for symbol in symbols] # Place orders to rebalance the portfolio. self.set_holdings(targets)