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GeezerVison is showing github.com — Lean/Algorithm.Python/BybitCryptoRegressionAlgorithm.py at master · QuantConnect/Lean Expand file tree/ Copy pathBybitCryptoRegressionAlgorithm.pyMore file actions 80 lines (62 loc) · 3.44 KB / Copy pathBybitCryptoRegressionAlgorithm.pyFile metadata and controls80 lines (62 loc) · 3.44 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 # 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}") |
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