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* Add Bybit brokerage model regression algorithm * Add BibytFutures brokerage model name and Bybit backtesting regression algorithms * Add Bybit margin calculations unit tests * Minor changes in data * Unify Bybit Spot and Futures brokerage model into one class * Add new Bybit configurations * Revert config change
81 lines
3.4 KiB
Python
81 lines
3.4 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### Algorithm demonstrating and ensuring that Bybit crypto brokerage model works as expected
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### </summary>
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class BybitCryptoRegressionAlgorithm(QCAlgorithm):
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def Initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.SetStartDate(2022, 12, 13)
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self.SetEndDate(2022, 12, 13)
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# Set account currency (USDT)
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self.SetAccountCurrency("USDT")
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# Set strategy cash (USD)
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self.SetCash(100000)
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# Add some coin as initial holdings
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# When connected to a real brokerage, the amount specified in SetCash
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# will be replaced with the amount in your actual account.
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self.SetCash("BTC", 1)
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self.SetBrokerageModel(BrokerageName.Bybit, AccountType.Cash)
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self.btcUsdt = self.AddCrypto("BTCUSDT").Symbol
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# create two moving averages
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self.fast = self.EMA(self.btcUsdt, 30, Resolution.Minute)
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self.slow = self.EMA(self.btcUsdt, 60, Resolution.Minute)
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self.liquidated = False
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def OnData(self, data):
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if self.Portfolio.CashBook["USDT"].ConversionRate == 0 or self.Portfolio.CashBook["BTC"].ConversionRate == 0:
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self.Log(f"USDT conversion rate: {self.Portfolio.CashBook['USDT'].ConversionRate}")
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self.Log(f"BTC conversion rate: {self.Portfolio.CashBook['BTC'].ConversionRate}")
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raise Exception("Conversion rate is 0")
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if not self.slow.IsReady:
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return
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btcAmount = self.Portfolio.CashBook["BTC"].Amount
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if self.fast > self.slow:
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if btcAmount == 1 and not self.liquidated:
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self.Buy(self.btcUsdt, 1)
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else:
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if btcAmount > 1:
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self.Liquidate(self.btcUsdt)
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self.liquidated = True
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elif btcAmount > 0 and self.liquidated and len(self.Transactions.GetOpenOrders()) == 0:
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# Place a limit order to sell our initial BTC holdings at 1% above the current price
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limitPrice = round(self.Securities[self.btcUsdt].Price * 1.01, 2)
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self.LimitOrder(self.btcUsdt, -btcAmount, limitPrice)
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def OnOrderEvent(self, orderEvent):
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self.Debug("{} {}".format(self.Time, orderEvent.ToString()))
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def OnEndOfAlgorithm(self):
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self.Log(f"{self.Time} - TotalPortfolioValue: {self.Portfolio.TotalPortfolioValue}")
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self.Log(f"{self.Time} - CashBook: {self.Portfolio.CashBook}")
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btcAmount = self.Portfolio.CashBook["BTC"].Amount
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if btcAmount > 0:
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raise Exception(f"BTC holdings should be zero at the end of the algorithm, but was {btcAmount}")
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