# 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 clr import AddReference

AddReference("System")

AddReference("QuantConnect.Algorithm")

AddReference("QuantConnect.Common")

from System import *

from QuantConnect import *

from QuantConnect.Algorithm import *

from QuantConnect.Brokerages import *

from QuantConnect.Data import *

from QuantConnect.Data.Market import *

from QuantConnect.Orders import *

### <summary>

### Demonstration of payments for cash dividends in backtesting. When data normalization mode is set

### to "Raw" the dividends are paid as cash directly into your portfolio.

### </summary>

### <meta name="tag" content="using data" />

### <meta name="tag" content="data event handlers" />

### <meta name="tag" content="dividend event" />

class DividendAlgorithm(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.SetStartDate(1998,1,1) #Set Start Date

self.SetEndDate(2006,1,21) #Set End Date

self.SetCash(100000) #Set Strategy Cash

# Find more symbols here: http://quantconnect.com/data

equity = self.AddEquity("MSFT", Resolution.Daily)

equity.SetDataNormalizationMode(DataNormalizationMode.Raw)

# this will use the Tradier Brokerage open order split behavior

# forward split will modify open order to maintain order value

# reverse split open orders will be cancelled

self.SetBrokerageModel(BrokerageName.TradierBrokerage)

def OnData(self, data):

'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''

bar = data["MSFT"]

if self.Transactions.OrdersCount == 0:

self.SetHoldings("MSFT", .5)

# place some orders that won't fill, when the split comes in they'll get modified to reflect the split

quantity = self.CalculateOrderQuantity("MSFT", .25)

self.Debug("Purchased Stock: {0}".format(bar.Price))

self.StopMarketOrder("MSFT", -quantity, bar.Low/2)

self.LimitOrder("MSFT", -quantity, bar.High*2)

for kvp in data.Dividends: # update this to Dividends dictionary

symbol = kvp.Key

value = kvp.Value.Distribution

self.Log("{0} >> DIVIDEND >> {1} - {2} - {3} - {4}".format(self.Time, symbol, value, self.Portfolio.Cash, self.Portfolio["MSFT"].Price))

for kvp in data.Splits: # update this to Splits dictionary

symbol = kvp.Key

value = kvp.Value.SplitFactor

self.Log("{0} >> SPLIT >> {1} - {2} - {3} - {4}".format(self.Time, symbol, value, self.Portfolio.Cash, self.Portfolio["MSFT"].Quantity))

def OnOrderEvent(self, orderEvent):

# orders get adjusted based on split events to maintain order value

order = self.Transactions.GetOrderById(orderEvent.OrderId)

self.Log("{0} >> ORDER >> {1}".format(self.Time, order))