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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.")