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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from AlgorithmImports import *
### <summary>
### Algorithm demonstrating and ensuring that Bybit crypto brokerage model works as expected
### </summary>
class BybitCryptoRegressionAlgorithm(QCAlgorithm):
def initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.set_start_date(2022, 12, 13)
self.set_end_date(2022, 12, 13)
# Set account currency (USDT)
self.set_account_currency("USDT")
# Set strategy cash (USD)
self.set_cash(100000)
# Add some coin as initial holdings
# When connected to a real brokerage, the amount specified in SetCash
# will be replaced with the amount in your actual account.
self.set_cash("BTC", 1)
self.set_brokerage_model(BrokerageName.BYBIT, AccountType.CASH)
self.btc_usdt = self.add_crypto("BTCUSDT").symbol
# create two moving averages
self.fast = self.ema(self.btc_usdt, 30, Resolution.MINUTE)
self.slow = self.ema(self.btc_usdt, 60, Resolution.MINUTE)
self.liquidated = False
def on_data(self, data):
if self.portfolio.cash_book["USDT"].conversion_rate == 0 or self.portfolio.cash_book["BTC"].conversion_rate == 0:
self.log(f"USDT conversion rate: {self.portfolio.cash_book['USDT'].conversion_rate}")
self.log(f"BTC conversion rate: {self.portfolio.cash_book['BTC'].conversion_rate}")
raise AssertionError("Conversion rate is 0")
if not self.slow.is_ready:
return
btc_amount = self.portfolio.cash_book["BTC"].amount
if self.fast > self.slow:
if btc_amount == 1 and not self.liquidated:
self.buy(self.btc_usdt, 1)
else:
if btc_amount > 1:
self.liquidate(self.btc_usdt)
self.liquidated = True
elif btc_amount > 0 and self.liquidated and len(self.transactions.get_open_orders()) == 0:
# Place a limit order to sell our initial BTC holdings at 1% above the current price
limit_price = round(self.securities[self.btc_usdt].price * 1.01, 2)
self.limit_order(self.btc_usdt, -btc_amount, limit_price)
def on_order_event(self, order_event):
self.debug("{} {}".format(self.time, order_event.to_string()))
def on_end_of_algorithm(self):
self.log(f"{self.time} - TotalPortfolioValue: {self.portfolio.total_portfolio_value}")
self.log(f"{self.time} - CashBook: {self.portfolio.cash_book}")
btc_amount = self.portfolio.cash_book["BTC"].amount
if btc_amount > 0:
raise AssertionError(f"BTC holdings should be zero at the end of the algorithm, but was {btc_amount}")