wehub-resource-sync
93 lines
4.4 KiB
Python
93 lines
4.4 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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from io import StringIO
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class ObjectStoreExampleAlgorithm(QCAlgorithm):
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'''This algorithm showcases some features of the IObjectStore feature.
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One use case is to make consecutive backtests run faster by caching the results of
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potentially time consuming operations. In this example, we save the results of a
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history call. This pattern can be equally applied to a machine learning model being
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trained and then saving the model weights in the object store.
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'''
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spy_close_object_store_key = "spy_close"
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spy_close_history = RollingWindow(252)
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spy_close_ema10_history = RollingWindow(252)
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spy_close_ema50_history = RollingWindow(252)
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def initialize(self):
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self.set_start_date(2013, 10, 7)
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self.set_end_date(2013, 10, 11)
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self.SPY = self.add_equity("SPY", Resolution.MINUTE).symbol
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self.spy_close = self.identity(self.SPY, Resolution.DAILY)
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self.spy_close_ema10 = IndicatorExtensions.ema(self.spy_close, 10)
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self.spy_close_ema50 = IndicatorExtensions.ema(self.spy_close, 50)
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# track last year of close and EMA10/EMA50
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self.spy_close.updated += lambda _, args: self.spy_close_history.add(args)
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self.spy_close_ema10.updated += lambda _, args: self.spy_close_ema10_history.add(args)
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self.spy_close_ema50.updated += lambda _, args: self.spy_close_ema50_history.add(args)
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if self.object_store.contains_key(self.spy_close_object_store_key):
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# our object store has our historical data saved, read the data
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# and push it through the indicators to warm everything up
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values = self.object_store.read(self.spy_close_object_store_key)
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self.debug(f'{self.spy_close_object_store_key} key exists in object store.')
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history = pd.read_csv(StringIO(values), header=None, index_col=0, squeeze=True)
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history.index = pd.to_datetime(history.index)
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for time, close in history.items():
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self.spy_close.update(time, close)
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else:
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self.debug(f'{self.spy_close_object_store_key} key does not exist in object store. Fetching history...')
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# if our object store doesn't have our data, fetch the history to initialize
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# we're pulling the last year's worth of SPY daily trade bars to fee into our indicators
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history = self.history(self.SPY, timedelta(365), Resolution.DAILY).close.unstack(0).squeeze()
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for time, close in history.items():
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self.spy_close.update(time, close)
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# save our warm up data so next time we don't need to issue the history request
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self.object_store.save(self.spy_close_object_store_key,
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'\n'.join(reversed([f'{x.end_time},{x.value}' for x in self.spy_close_history])))
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# Can also use ObjectStore.save_bytes(key, byte[])
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# and to read ObjectStore.read_bytes(key) => byte[]
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# we can also get a file path for our data. some ML libraries require model
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# weights to be loaded directly from a file path. The object store can provide
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# a file path for any key by: ObjectStore.get_file_path(key) => string (file path)
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def on_data(self, slice):
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close = self.spy_close
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ema10 = self.spy_close_ema10
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ema50 = self.spy_close_ema50
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if ema10 > close and ema10 > ema50:
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self.set_holdings(self.SPY, 1)
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elif ema10 < close and ema10 < ema50:
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self.set_holdings(self.SPY, -1)
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elif ema10 < ema50 and self.portfolio[self.SPY].is_long:
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self.liquidate(self.SPY)
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elif ema10 > ema50 and self.portfolio[self.SPY].is_short:
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self.liquidate(self.SPY)
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