Derek Melchin
76 lines
3.1 KiB
Python
76 lines
3.1 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import QCAlgorithm
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import pandas as pd
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import numpy as np
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import talib
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class CalibratedResistanceAtmosphericScrubbers(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2020, 1, 2)
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self.SetEndDate(2020, 1, 6)
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self.SetCash(100000)
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self.AddEquity("SPY", Resolution.Hour)
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self.rolling_window = pd.DataFrame()
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self.dema_period = 3
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self.sma_period = 3
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self.wma_period = 3
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self.window_size = self.dema_period * 2
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self.SetWarmUp(self.window_size)
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def OnData(self, data):
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if "SPY" not in data.Bars:
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return
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close = data["SPY"].Close
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if self.IsWarmingUp:
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# Add latest close to rolling window
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row = pd.DataFrame({"close": [close]}, index=[data.Time])
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self.rolling_window = self.rolling_window.append(row).iloc[-self.window_size:]
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# If we have enough closing data to start calculating indicators...
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if self.rolling_window.shape[0] == self.window_size:
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closes = self.rolling_window['close'].values
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# Add indicator columns to DataFrame
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self.rolling_window['DEMA'] = talib.DEMA(closes, self.dema_period)
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self.rolling_window['EMA'] = talib.EMA(closes, self.sma_period)
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self.rolling_window['WMA'] = talib.WMA(closes, self.wma_period)
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return
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closes = np.append(self.rolling_window['close'].values, close)[-self.window_size:]
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# Update talib indicators time series with the latest close
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row = pd.DataFrame({"close": close,
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"DEMA" : talib.DEMA(closes, self.dema_period)[-1],
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"EMA" : talib.EMA(closes, self.sma_period)[-1],
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"WMA" : talib.WMA(closes, self.wma_period)[-1]},
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index=[data.Time])
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self.rolling_window = self.rolling_window.append(row).iloc[-self.window_size:]
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def OnEndOfAlgorithm(self):
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self.Log(f"\nRolling Window:\n{self.rolling_window.to_string()}\n")
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self.Log(f"\nLatest Values:\n{self.rolling_window.iloc[-1].to_string()}\n") |