wehub-resource-sync
134 lines
6.1 KiB
Python
134 lines
6.1 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### Demonstration of how to define a universe using the fundamental data
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="universes" />
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### <meta name="tag" content="coarse universes" />
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### <meta name="tag" content="regression test" />
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class FundamentalRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2014, 3, 26)
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self.set_end_date(2014, 4, 7)
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self.universe_settings.resolution = Resolution.DAILY
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self._universe = self.add_universe(self.selection_function)
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# before we add any symbol
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self.assert_fundamental_universe_data()
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self.add_equity("SPY")
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self.add_equity("AAPL")
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# Request fundamental data for symbols at current algorithm time
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ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
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ibm_fundamental = self.fundamentals(ibm)
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if self.time != self.start_date or self.time != ibm_fundamental.end_time:
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raise ValueError(f"Unexpected Fundamental time {ibm_fundamental.end_time}")
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if ibm_fundamental.price == 0:
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raise ValueError(f"Unexpected Fundamental IBM price!")
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nb = Symbol.create("NB", SecurityType.EQUITY, Market.USA)
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fundamentals = self.fundamentals([ nb, ibm ])
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if len(fundamentals) != 2:
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raise ValueError(f"Unexpected Fundamental count {len(fundamentals)}! Expected 2")
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# Request historical fundamental data for symbols
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history = self.history(Fundamental, timedelta(days=2))
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if len(history) != 4:
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raise ValueError(f"Unexpected Fundamental history count {len(history)}! Expected 4")
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for ticker in [ "AAPL", "SPY" ]:
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data = history.loc[ticker]
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if data["value"][0] == 0:
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raise ValueError(f"Unexpected {data} fundamental data")
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if Object.reference_equals(data.earningreports.iloc[0], data.earningreports.iloc[1]):
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raise ValueError(f"Unexpected fundamental data instance duplication")
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if data.earningreports.iloc[0]._time_provider.get_utc_now() == data.earningreports.iloc[1]._time_provider.get_utc_now():
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raise ValueError(f"Unexpected fundamental data instance duplication")
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self.assert_fundamental_universe_data()
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self.changes = None
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self.number_of_symbols_fundamental = 2
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def assert_fundamental_universe_data(self):
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# Case A
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universe_data = self.history(self._universe.data_type, [self._universe.symbol], timedelta(days=2), flatten=True)
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self.assert_fundamental_history(universe_data, "A")
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# Case B (sugar on A)
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universe_data_per_time = self.history(self._universe, timedelta(days=2), flatten=True)
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self.assert_fundamental_history(universe_data_per_time, "B")
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# Case C: Passing through the unvierse type and symbol
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enumerable_of_data_dictionary = self.history[self._universe.data_type]([self._universe.symbol], 100)
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for selection_collection_for_a_day in enumerable_of_data_dictionary:
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self.assert_fundamental_enumerator(selection_collection_for_a_day[self._universe.symbol], "C")
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def assert_fundamental_history(self, df, case_name):
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dates = df.index.get_level_values('time').unique()
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if dates.shape[0] != 2:
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raise ValueError(f"Unexpected Fundamental universe dates count {dates.shape[0]}! Expected 2")
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for date in dates:
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sub_df = df.loc[date]
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if sub_df.shape[0] < 7000:
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raise ValueError(f"Unexpected historical Fundamentals data count {sub_df.shape[0]} case {case_name}! Expected > 7000")
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def assert_fundamental_enumerator(self, enumerable, case_name):
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data_point_count = 0
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for fundamental in enumerable:
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data_point_count += 1
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if type(fundamental) is not Fundamental:
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raise ValueError(f"Unexpected Fundamentals data type {type(fundamental)} case {case_name}! {str(fundamental)}")
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if data_point_count < 7000:
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raise ValueError(f"Unexpected historical Fundamentals data count {data_point_count} case {case_name}! Expected > 7000")
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# return a list of three fixed symbol objects
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def selection_function(self, fundamental):
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# sort descending by daily dollar volume
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sorted_by_dollar_volume = sorted([x for x in fundamental if x.price > 1],
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key=lambda x: x.dollar_volume, reverse=True)
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# sort descending by P/E ratio
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sorted_by_pe_ratio = sorted(sorted_by_dollar_volume, key=lambda x: x.valuation_ratios.pe_ratio, reverse=True)
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# take the top entries from our sorted collection
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return [ x.symbol for x in sorted_by_pe_ratio[:self.number_of_symbols_fundamental] ]
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def on_data(self, data):
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# if we have no changes, do nothing
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if self.changes is None: return
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# liquidate removed securities
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for security in self.changes.removed_securities:
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if security.invested:
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self.liquidate(security.symbol)
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self.debug("Liquidated Stock: " + str(security.symbol.value))
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# we want 50% allocation in each security in our universe
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for security in self.changes.added_securities:
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self.set_holdings(security.symbol, 0.02)
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self.changes = None
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# this event fires whenever we have changes to our universe
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def on_securities_changed(self, changes):
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self.changes = changes
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