wehub-resource-sync
103 lines
4.5 KiB
Python
103 lines
4.5 KiB
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>
|
|
### CapmAlphaRankingFrameworkAlgorithm: example of custom scheduled universe selection model
|
|
### Universe Selection inspired by https://www.quantconnect.com/tutorials/strategy-library/capm-alpha-ranking-strategy-on-dow-30-companies
|
|
### </summary>
|
|
class CapmAlphaRankingFrameworkAlgorithm(QCAlgorithm):
|
|
'''CapmAlphaRankingFrameworkAlgorithm: example of custom scheduled universe selection model'''
|
|
|
|
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.'''
|
|
|
|
# Set requested data resolution
|
|
self.universe_settings.resolution = Resolution.MINUTE
|
|
|
|
self.set_start_date(2016, 1, 1) #Set Start Date
|
|
self.set_end_date(2017, 1, 1) #Set End Date
|
|
self.set_cash(100000) #Set Strategy Cash
|
|
|
|
# set algorithm framework models
|
|
self.set_universe_selection(CapmAlphaRankingUniverseSelectionModel())
|
|
self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(1), 0.025, None))
|
|
self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
|
|
self.set_execution(ImmediateExecutionModel())
|
|
self.set_risk_management(MaximumDrawdownPercentPerSecurity(0.01))
|
|
|
|
class CapmAlphaRankingUniverseSelectionModel(UniverseSelectionModel):
|
|
'''This universe selection model picks stocks with the highest alpha: interception of the linear regression against a benchmark.'''
|
|
|
|
period = 21
|
|
benchmark = "SPY"
|
|
|
|
# Symbols of Dow 30 companies.
|
|
_symbols = [Symbol.create(x, SecurityType.EQUITY, Market.USA)
|
|
for x in ["AAPL", "AXP", "BA", "CAT", "CSCO", "CVX", "DD", "DIS", "GE", "GS",
|
|
"HD", "IBM", "INTC", "JPM", "KO", "MCD", "MMM", "MRK", "MSFT",
|
|
"NKE","PFE", "PG", "TRV", "UNH", "UTX", "V", "VZ", "WMT", "XOM"]]
|
|
|
|
def create_universes(self, algorithm):
|
|
|
|
# Adds the benchmark to the user defined universe
|
|
benchmark = algorithm.add_equity(self.benchmark, Resolution.DAILY)
|
|
|
|
# Defines a schedule universe that fires after market open when the month starts
|
|
return [ ScheduledUniverse(
|
|
benchmark.exchange.time_zone,
|
|
algorithm.date_rules.month_start(self.benchmark),
|
|
algorithm.time_rules.after_market_open(self.benchmark),
|
|
lambda datetime: self.select_pair(algorithm, datetime),
|
|
algorithm.universe_settings)]
|
|
|
|
def select_pair(self, algorithm, date):
|
|
'''Selects the pair (two stocks) with the highest alpha'''
|
|
dictionary = dict()
|
|
benchmark = self._get_returns(algorithm, self.benchmark)
|
|
ones = np.ones(len(benchmark))
|
|
|
|
for symbol in self._symbols:
|
|
prices = self._get_returns(algorithm, symbol)
|
|
if prices is None: continue
|
|
A = np.vstack([prices, ones]).T
|
|
|
|
# Calculate the Least-Square fitting to the returns of a given symbol and the benchmark
|
|
ols = np.linalg.lstsq(A, benchmark)[0]
|
|
dictionary[symbol] = ols[1]
|
|
|
|
# Returns the top 2 highest alphas
|
|
ordered_dictionary = sorted(dictionary.items(), key= lambda x: x[1], reverse=True)
|
|
return [x[0] for x in ordered_dictionary[:2]]
|
|
|
|
def _get_returns(self, algorithm, symbol):
|
|
|
|
history = algorithm.history([symbol], self.period, Resolution.DAILY)
|
|
if history.empty: return None
|
|
|
|
window = RollingWindow(self.period)
|
|
rate_of_change = RateOfChange(1)
|
|
|
|
def roc_updated(s, item):
|
|
window.add(item.value)
|
|
|
|
rate_of_change.updated += roc_updated
|
|
|
|
history = history.close.reset_index(level=0, drop=True).items()
|
|
|
|
for time, value in history:
|
|
rate_of_change.update(time, value)
|
|
|
|
return [ x for x in window]
|