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