wehub-resource-sync
84 lines
3.7 KiB
Python
84 lines
3.7 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 *
|
|
|
|
class SmaCrossUniverseSelectionAlgorithm(QCAlgorithm):
|
|
'''Provides an example where WarmUpIndicator method is used to warm up indicators
|
|
after their security is added and before (Universe Selection scenario)'''
|
|
|
|
_count = 10
|
|
_tolerance = 0.01
|
|
_target_percent = 1 / _count
|
|
_averages = dict()
|
|
|
|
def initialize(self) -> None:
|
|
self.universe_settings.leverage = 2
|
|
self.universe_settings.resolution = Resolution.DAILY
|
|
|
|
self.set_start_date(2018, 1, 1)
|
|
self.set_end_date(2019, 1, 1)
|
|
self.set_cash(1000000)
|
|
|
|
self.settings.automatic_indicator_warm_up = True
|
|
|
|
ibm = self.add_equity("IBM", Resolution.HOUR).symbol
|
|
ibm_sma = self.sma(ibm, 40)
|
|
self.log(f"{ibm_sma.name}: {ibm_sma.current.time} - {ibm_sma}. IsReady? {ibm_sma.is_ready}")
|
|
|
|
spy = self.add_equity("SPY", Resolution.HOUR).symbol
|
|
spy_sma = self.sma(spy, 10) # Data point indicator
|
|
spy_atr = self.atr(spy, 10,) # Bar indicator
|
|
spy_vwap = self.vwap(spy, 10) # TradeBar indicator
|
|
self.log(f"SPY - Is ready? SMA: {spy_sma.is_ready}, ATR: {spy_atr.is_ready}, VWAP: {spy_vwap.is_ready}")
|
|
|
|
eur = self.add_forex("EURUSD", Resolution.HOUR).symbol
|
|
eur_sma = self.sma(eur, 20, Resolution.DAILY)
|
|
eur_atr = self.atr(eur, 20, MovingAverageType.SIMPLE, Resolution.DAILY)
|
|
self.log(f"EURUSD - Is ready? SMA: {eur_sma.is_ready}, ATR: {eur_atr.is_ready}")
|
|
|
|
self.add_universe(self.coarse_sma_selector)
|
|
|
|
# Since the indicators are ready, we will receive error messages
|
|
# reporting that the algorithm manager is trying to add old information
|
|
self.set_warm_up(10)
|
|
|
|
def coarse_sma_selector(self, coarse: list[Fundamental]) -> list[Symbol]:
|
|
score = dict()
|
|
for cf in coarse:
|
|
if not cf.has_fundamental_data:
|
|
continue
|
|
symbol = cf.symbol
|
|
price = cf.adjusted_price
|
|
# grab the SMA instance for this symbol
|
|
avg = self._averages.setdefault(symbol, SimpleMovingAverage(100))
|
|
self.warm_up_indicator(symbol, avg, Resolution.DAILY)
|
|
# Update returns true when the indicators are ready, so don't accept until they are
|
|
if avg.update(cf.end_time, price):
|
|
value = avg.current.value
|
|
# only pick symbols who have their price over their 100 day sma
|
|
if value > price * self._tolerance:
|
|
score[symbol] = (value - price) / ((value + price) / 2)
|
|
|
|
# prefer symbols with a larger delta by percentage between the two _averages
|
|
sorted_score = sorted(score.items(), key=lambda kvp: kvp[1], reverse=True)
|
|
return [x[0] for x in sorted_score[:self._count]]
|
|
|
|
def on_securities_changed(self, changes: SecurityChanges) -> None:
|
|
for security in changes.removed_securities:
|
|
if security.invested:
|
|
self.liquidate(security.symbol)
|
|
|
|
for security in changes.added_securities:
|
|
self.set_holdings(security.symbol, self._target_percent)
|