# 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 *
import talib

class CalibratedResistanceAtmosphericScrubbers(QCAlgorithm):

 def initialize(self):
 self.set_start_date(2020, 1, 2)
 self.set_end_date(2020, 1, 6) 
 self.set_cash(100000) 
 self.add_equity("SPY", Resolution.HOUR)
 
 self.rolling_window = pd.DataFrame()
 self.dema_period = 3
 self.sma_period = 3
 self.wma_period = 3
 self.window_size = self.dema_period * 2
 self.set_warm_up(self.window_size)
 
 def on_data(self, data):
 if "SPY" not in data.bars:
 return
 
 close = data["SPY"].close
 
 if self.is_warming_up:
 # Add latest close to rolling window
 row = pd.DataFrame({"close": [close]}, index=[data.time])
 self.rolling_window = pd.concat([self.rolling_window, row]).iloc[-self.window_size:]
 
 # If we have enough closing data to start calculating indicators...
 if self.rolling_window.shape[0] == self.window_size:
 closes = self.rolling_window['close'].values
 
 # Add indicator columns to DataFrame
 self.rolling_window['DEMA'] = talib.DEMA(closes, self.dema_period)
 self.rolling_window['EMA'] = talib.EMA(closes, self.sma_period)
 self.rolling_window['WMA'] = talib.WMA(closes, self.wma_period)
 return
 
 closes = np.append(self.rolling_window['close'].values, close)[-self.window_size:]
 
 # Update talib indicators time series with the latest close
 row = pd.DataFrame({"close": close,
 "DEMA" : talib.DEMA(closes, self.dema_period)[-1],
 "EMA" : talib.EMA(closes, self.sma_period)[-1],
 "WMA" : talib.WMA(closes, self.wma_period)[-1]},
 index=[data.time])
 
 self.rolling_window = pd.concat([self.rolling_window, row]).iloc[-self.window_size:]

 
 def on_end_of_algorithm(self):
 self.log(f"\nRolling Window:\n{self.rolling_window.to_string()}\n")
 self.log(f"\nLatest Values:\n{self.rolling_window.iloc[-1].to_string()}\n")