chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
5008 changed files with 910633 additions and 0 deletions
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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}")