chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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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>
+### Basic Template India Index Algorithm uses framework components to define the algorithm.
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="using quantconnect" />
+### <meta name="tag" content="trading and orders" />
+class BasicTemplateIndiaIndexAlgorithm(QCAlgorithm):
+    '''Basic template framework algorithm uses framework components to define the algorithm.'''
+
+    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_account_currency("INR") #Set Account Currency
+        self.set_start_date(2019, 1, 1)  #Set Start Date
+        self.set_end_date(2019, 1, 5)    #Set End Date
+        self.set_cash(1000000)          #Set Strategy Cash
+
+        # Use indicator for signal; but it cannot be traded
+        self.nifty = self.add_index("NIFTY50", Resolution.MINUTE, Market.INDIA).symbol
+        # Trade Index based ETF
+        self.nifty_etf = self.add_equity("JUNIORBEES", Resolution.MINUTE, Market.INDIA).symbol
+   
+        # Set Order Properties as per the requirements for order placement
+        self.default_order_properties = IndiaOrderProperties(Exchange.NSE)
+
+        # Define indicator
+        self._ema_slow = self.ema(self.nifty, 80)
+        self._ema_fast = self.ema(self.nifty, 200)
+
+        self.debug("numpy test >>> print numpy.pi: " + str(np.pi))
+
+
+    def on_data(self, data):
+        '''on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+
+        Arguments:
+            data: Slice object keyed by symbol containing the stock data
+        '''
+
+        if not data.bars.contains_key(self.nifty) or not data.bars.contains_key(self.nifty_etf):
+            return
+
+        if not self._ema_slow.is_ready:
+            return
+
+        if self._ema_fast > self._ema_slow:
+            if not self.portfolio.invested:
+                self.market_ticket = self.market_order(self.nifty_etf, 1)
+        else:
+            self.liquidate()
+
+
+    def on_end_of_algorithm(self):
+        if self.portfolio[self.nifty].total_sale_volume > 0:
+            raise AssertionError("Index is not tradable.")
+