# 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 *
from QuantConnect.Data.Auxiliary import *
from QuantConnect.Lean.Engine.DataFeeds import DefaultDataProvider

_ticker = "GOOGL"
_expected_raw_prices = [ 1158.72,
1131.97, 1114.28, 1120.15, 1114.51, 1134.89, 1135.1, 571.50, 545.25, 540.63 ]

# <summary>
# In this algorithm we demonstrate how to use the raw data for our securities
# and verify that the behavior is correct.
# </summary>
# <meta name="tag" content="using data" />
# <meta name="tag" content="regression test" />
class RawDataRegressionAlgorithm(QCAlgorithm):

 def initialize(self):
 self.set_start_date(2014, 3, 25)
 self.set_end_date(2014, 4, 7)
 self.set_cash(100000)

 # Set our DataNormalizationMode to raw
 self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
 self._googl = self.add_equity(_ticker, Resolution.DAILY).symbol

 # Get our factor file for this regression
 data_provider = DefaultDataProvider()
 map_file_provider = LocalDiskMapFileProvider()
 map_file_provider.initialize(data_provider)
 factor_file_provider = LocalDiskFactorFileProvider()
 factor_file_provider.initialize(map_file_provider, data_provider)

 # Get our factor file for this regression
 self._factor_file = factor_file_provider.get(self._googl)


 def on_data(self, data):
 if not self.portfolio.invested:
 self.set_holdings(self._googl, 1)

 if data.bars.contains_key(self._googl):
 googl_data = data.bars[self._googl]

 # Assert our volume matches what we expected
 expected_raw_price = _expected_raw_prices.pop(0)
 if expected_raw_price != googl_data.close:
 # Our values don't match lets try and give a reason why
 day_factor = self._factor_file.get_price_factor(googl_data.time, DataNormalizationMode.ADJUSTED)
 probable_raw_price = googl_data.close / day_factor # Undo adjustment

 raise AssertionError("Close price was incorrect; it appears to be the adjusted value"
 if expected_raw_price == probable_raw_price else
 "Close price was incorrect; Data may have changed.")