# 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 how to define a universe using the fundamental data
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
### <meta name="tag" content="regression test" />
class FundamentalRegressionAlgorithm(QCAlgorithm):

 def initialize(self):
 self.set_start_date(2014, 3, 26)
 self.set_end_date(2014, 4, 7)

 self.universe_settings.resolution = Resolution.DAILY

 self._universe = self.add_universe(self.selection_function)

 # before we add any symbol
 self.assert_fundamental_universe_data()

 self.add_equity("SPY")
 self.add_equity("AAPL")

 # Request fundamental data for symbols at current algorithm time
 ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
 ibm_fundamental = self.fundamentals(ibm)
 if self.time != self.start_date or self.time != ibm_fundamental.end_time:
 raise ValueError(f"Unexpected Fundamental time {ibm_fundamental.end_time}")
 if ibm_fundamental.price == 0:
 raise ValueError(f"Unexpected Fundamental IBM price!")
 nb = Symbol.create("NB", SecurityType.EQUITY, Market.USA)
 fundamentals = self.fundamentals([ nb, ibm ])
 if len(fundamentals) != 2:
 raise ValueError(f"Unexpected Fundamental count {len(fundamentals)}! Expected 2")

 # Request historical fundamental data for symbols
 history = self.history(Fundamental, timedelta(days=2))
 if len(history) != 4:
 raise ValueError(f"Unexpected Fundamental history count {len(history)}! Expected 4")

 for ticker in [ "AAPL", "SPY" ]:
 data = history.loc[ticker]
 if data["value"][0] == 0:
 raise ValueError(f"Unexpected {data} fundamental data")
 if Object.reference_equals(data.earningreports.iloc[0], data.earningreports.iloc[1]):
 raise ValueError(f"Unexpected fundamental data instance duplication")
 if data.earningreports.iloc[0]._time_provider.get_utc_now() == data.earningreports.iloc[1]._time_provider.get_utc_now():
 raise ValueError(f"Unexpected fundamental data instance duplication")

 self.assert_fundamental_universe_data()

 self.changes = None
 self.number_of_symbols_fundamental = 2

 def assert_fundamental_universe_data(self):
 # Case A
 universe_data = self.history(self._universe.data_type, [self._universe.symbol], timedelta(days=2), flatten=True)
 self.assert_fundamental_history(universe_data, "A")

 # Case B (sugar on A)
 universe_data_per_time = self.history(self._universe, timedelta(days=2), flatten=True)
 self.assert_fundamental_history(universe_data_per_time, "B")

 # Case C: Passing through the unvierse type and symbol
 enumerable_of_data_dictionary = self.history[self._universe.data_type]([self._universe.symbol], 100)
 for selection_collection_for_a_day in enumerable_of_data_dictionary:
 self.assert_fundamental_enumerator(selection_collection_for_a_day[self._universe.symbol], "C")

 def assert_fundamental_history(self, df, case_name):
 dates = df.index.get_level_values('time').unique()
 if dates.shape[0] != 2:
 raise ValueError(f"Unexpected Fundamental universe dates count {dates.shape[0]}! Expected 2")

 for date in dates:
 sub_df = df.loc[date]
 if sub_df.shape[0] < 7000:
 raise ValueError(f"Unexpected historical Fundamentals data count {sub_df.shape[0]} case {case_name}! Expected > 7000")

 def assert_fundamental_enumerator(self, enumerable, case_name):
 data_point_count = 0
 for fundamental in enumerable:
 data_point_count += 1
 if type(fundamental) is not Fundamental:
 raise ValueError(f"Unexpected Fundamentals data type {type(fundamental)} case {case_name}! {str(fundamental)}")
 if data_point_count < 7000:
 raise ValueError(f"Unexpected historical Fundamentals data count {data_point_count} case {case_name}! Expected > 7000")

 # return a list of three fixed symbol objects
 def selection_function(self, fundamental):
 # sort descending by daily dollar volume
 sorted_by_dollar_volume = sorted([x for x in fundamental if x.price > 1],
 key=lambda x: x.dollar_volume, reverse=True)

 # sort descending by P/E ratio
 sorted_by_pe_ratio = sorted(sorted_by_dollar_volume, key=lambda x: x.valuation_ratios.pe_ratio, reverse=True)

 # take the top entries from our sorted collection
 return [ x.symbol for x in sorted_by_pe_ratio[:self.number_of_symbols_fundamental] ]

 def on_data(self, data):
 # if we have no changes, do nothing
 if self.changes is None: return

 # liquidate removed securities
 for security in self.changes.removed_securities:
 if security.invested:
 self.liquidate(security.symbol)
 self.debug("Liquidated Stock: " + str(security.symbol.value))

 # we want 50% allocation in each security in our universe
 for security in self.changes.added_securities:
 self.set_holdings(security.symbol, 0.02)

 self.changes = None

 # this event fires whenever we have changes to our universe
 def on_securities_changed(self, changes):
 self.changes = changes