Donchian Breakouts Filtered by an OBV Oscillator for Long and Short Trades

# DelistingEventsAlgorithm


# DelistingEventsAlgorithm









Demonstration of using the Delisting event in your algorithm. Assets are delisted on their last day of trading, or when their contract expires. This data is not included in the open source project.

## Source (Apache-2.0)

```python
# 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>
### Demonstration of using the Delisting event in your algorithm. Assets are delisted on their last day of trading, or when their contract expires.
### This data is not included in the open source project.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="data event handlers" />
### <meta name="tag" content="delisting event" />
class DelistingEventsAlgorithm(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(2007, 5, 15)  #Set Start Date
        self.set_end_date(2007, 5, 25)    #Set End Date
        self.set_cash(100000)             #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.add_equity("AAA.1", Resolution.DAILY)
        self.add_equity("SPY", Resolution.DAILY)


    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 self.transactions.orders_count == 0:
            self.set_holdings("AAA.1", 1)
            self.debug("Purchased stock")

        for kvp in data.bars:
            symbol = kvp.key
            value = kvp.value

            self.log("OnData(Slice): {0}: {1}: {2}".format(self.time, symbol, value.close))

        # the slice can also contain delisting data: data.delistings in a dictionary string->Delisting

        aaa = self.securities["AAA.1"]
        if aaa.is_delisted and aaa.is_tradable:
            raise AssertionError("Delisted security must NOT be tradable")

        if not aaa.is_delisted and not aaa.is_tradable:
            raise AssertionError("Securities must be marked as tradable until they're delisted or removed from the universe")

        for kvp in data.delistings:
            symbol = kvp.key
            value = kvp.value

            if value.type == DelistingType.WARNING:
                self.log("OnData(Delistings): {0}: {1} will be delisted at end of day today.".format(self.time, symbol))

                # liquidate on delisting warning
                self.set_holdings(symbol, 0)

            if value.type == DelistingType.DELISTED:
                self.log("OnData(Delistings): {0}: {1} has been delisted.".format(self.time, symbol))

                # fails because the security has already been delisted and is no longer tradable
                self.set_holdings(symbol, 1)


    def on_order_event(self, order_event):
        self.log("OnOrderEvent(OrderEvent): {0}: {1}".format(self.time, order_event))

```