# 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 *
from io import StringIO

class ObjectStoreExampleAlgorithm(QCAlgorithm):
 '''This algorithm showcases some features of the IObjectStore feature.
 One use case is to make consecutive backtests run faster by caching the results of
 potentially time consuming operations. In this example, we save the results of a
 history call. This pattern can be equally applied to a machine learning model being
 trained and then saving the model weights in the object store.
 '''
 spy_close_object_store_key = "spy_close"
 spy_close_history = RollingWindow(252)
 spy_close_ema10_history = RollingWindow(252)
 spy_close_ema50_history = RollingWindow(252)

 def initialize(self):
 self.set_start_date(2013, 10, 7)
 self.set_end_date(2013, 10, 11)

 self.SPY = self.add_equity("SPY", Resolution.MINUTE).symbol

 self.spy_close = self.identity(self.SPY, Resolution.DAILY)
 self.spy_close_ema10 = IndicatorExtensions.ema(self.spy_close, 10)
 self.spy_close_ema50 = IndicatorExtensions.ema(self.spy_close, 50)

 # track last year of close and EMA10/EMA50
 self.spy_close.updated += lambda _, args: self.spy_close_history.add(args)
 self.spy_close_ema10.updated += lambda _, args: self.spy_close_ema10_history.add(args)
 self.spy_close_ema50.updated += lambda _, args: self.spy_close_ema50_history.add(args)

 if self.object_store.contains_key(self.spy_close_object_store_key):
 # our object store has our historical data saved, read the data
 # and push it through the indicators to warm everything up
 values = self.object_store.read(self.spy_close_object_store_key)
 self.debug(f'{self.spy_close_object_store_key} key exists in object store.')

 history = pd.read_csv(StringIO(values), header=None, index_col=0, squeeze=True)
 history.index = pd.to_datetime(history.index)
 for time, close in history.items():
 self.spy_close.update(time, close)

 else:
 self.debug(f'{self.spy_close_object_store_key} key does not exist in object store. Fetching history...')

 # if our object store doesn't have our data, fetch the history to initialize
 # we're pulling the last year's worth of SPY daily trade bars to fee into our indicators
 history = self.history(self.SPY, timedelta(365), Resolution.DAILY).close.unstack(0).squeeze()

 for time, close in history.items():
 self.spy_close.update(time, close)

 # save our warm up data so next time we don't need to issue the history request
 self.object_store.save(self.spy_close_object_store_key,
 '\n'.join(reversed([f'{x.end_time},{x.value}' for x in self.spy_close_history])))

 # Can also use ObjectStore.save_bytes(key, byte[])
 # and to read ObjectStore.read_bytes(key) => byte[]

 # we can also get a file path for our data. some ML libraries require model
 # weights to be loaded directly from a file path. The object store can provide
 # a file path for any key by: ObjectStore.get_file_path(key) => string (file path)

 def on_data(self, slice):

 close = self.spy_close
 ema10 = self.spy_close_ema10
 ema50 = self.spy_close_ema50

 if ema10 > close and ema10 > ema50:
 self.set_holdings(self.SPY, 1)

 elif ema10 < close and ema10 < ema50:
 self.set_holdings(self.SPY, -1)

 elif ema10 < ema50 and self.portfolio[self.SPY].is_long:
 self.liquidate(self.SPY)

 elif ema10 > ema50 and self.portfolio[self.SPY].is_short:
 self.liquidate(self.SPY)