from datetime import datetime
import decimal
import numpy as np
class DynamicBreakoutAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018,1,1)
self.SetEndDate(datetime.now())
self.SetCash(100000)
Equity = self.AddEquity("AAPL", Resolution.Daily)
Equity = self.AddEquity("NVDA", Resolution.Daily)
self.syl = Equity.Symbol
self.Schedule.On(self.DateRules.EveryDay(self.syl), self.TimeRules.BeforeMarketClose(self.syl,1),Action(self.SetSignal))
self.numdays = 20
self.ceiling,self.floor = 60,20
self.buypoint, self.sellpoint= None, None
self.longLiqPoint, self.shortLiqPoint, self.yesterdayclose= None, None, None
self.SetBenchmark(self.syl)
self.Bolband = self.BB(self.syl, self.numdays, 2, MovingAverageType.Simple, Resolution.Daily)
self.__macd = self.MACD("AAPL", 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
self.__macd = self.MACD("NVDA", 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
self.__previous = datetime.min
self.PlotIndicator("MACD", True, self.__macd, self.__macd.Signal)
self.PlotIndicator("AAPL", self.__macd.Fast, self.__macd.Slow)
self.PlotIndicator("NVDA", self.__macd.Fast, self.__macd.Slow)
self.aapl = self.AddEquity("AAPL", Resolution.Daily).Symbol
self.nvda = self.AddEquity("NVDA", Resolution.Daily).Symbol
self.cache = {
self.aapl: SymbolData(self.aapl, self),
self.nvda: SymbolData(self.nvda, self)
}
def SetSignal(self):
close = self.History(self.syl, 31, Resolution.Daily)['close']
todayvol = np.std(close[1:31])
yesterdayvol = np.std(close[0:30])
deltavol = (todayvol - yesterdayvol) / todayvol
self.numdays = int(round(self.numdays * (1 + deltavol)))
if self.numdays > self.ceiling:
self.numdays = self.ceiling
elif self.numdays < self.floor:
self.numdays = self.floor
self.high = self.History(self.syl, self.numdays, Resolution.Daily)['high']
self.low = self.History(self.syl, self.numdays, Resolution.Daily)['low']
self.buypoint = max(self.high)
self.sellpoint = min(self.low)
historyclose = self.History(self.syl, self.numdays, Resolution.Daily)['close']
self.longLiqPoint = np.mean(historyclose)
self.shortLiqPoint = np.mean(historyclose)
self.yesterdayclose = historyclose.iloc[-1]
# wait for our BollingerBand to fully initialize
if not self.Bolband.IsReady: return
holdings = self.Portfolio[self.syl].Quantity
if self.yesterdayclose > self.Bolband.UpperBand.Current.Value and self.Portfolio[self.syl].Price >= self.buypoint:
self.SetHoldings(self.syl, 1)
elif self.yesterdayclose < self.Bolband.LowerBand.Current.Value and self.Portfolio[self.syl].Price <= self.sellpoint:
self.SetHoldings(self.syl, -1)
if holdings > 0 and self.Portfolio[self.syl].Price <= self.shortLiqPoint:
self.Liquidate(self.syl)
elif holdings < 0 and self.Portfolio[self.syl].Price >= self.shortLiqPoint:
self.Liquidate(self.syl)
self.Log(str(self.yesterdayclose)+(" # of days ")+(str(self.numdays)))
def OnData(self,data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# wait for our macd to fully initialize
if not self.cache[self.syl].MACD: return
# only once per day
if self.__previous.date() == self.Time.date(): return
# define a small tolerance on our checks to avoid bouncing
tolerance = 0.0025
holdings = self.Portfolio["AAPL"].Quantity
holdings = self.Portfolio["NVDA"].Quantity
signalDeltaPercent = (self.__macd.Current.Value - self.__macd.Signal.Current.Value)/self.__macd.Fast.Current.Value
# if our macd is greater than our signal, then let's go long
if holdings <= 0 and signalDeltaPercent > tolerance: # 0.01%
# longterm says buy as well
self.SetHoldings("AAPL", 1.0)
self.SetHoldings("NVDA", 1.0)
# of our macd is less than our signal, then let's go short
elif holdings >= 0 and signalDeltaPercent < -tolerance:
self.Liquidate("AAPL")
self.Liquidate("NVDA")
self.__previous = self.Timefrom datetime import datetime
import decimal
import numpy as np
from Alphas.MacdAlphaModel import MacdAlphaModel
class SymbolData:
def __init__(self, symbol, algorithm):
self.Symbol = symbol
self.MACD = algorithm.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
self.buypoint, self.sellpoint= None, None
self.longLiqPoint, self.shortLiqPoint, self.yesterdayclose= None, None, None
self.numdays = 30
self.Bolband = algorithm.BB(symbol, self.numdays, 2, MovingAverageType.Simple, Resolution.Daily)
self.__previous = datetime.min
class OptimizedDynamicRegulators(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018, 10, 25) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
# self.AddEquity("SPY", Resolution.Minute)
self.AddAlpha(MacdAlphaModel(12, 26, 9, MovingAverageType.Exponential, Resolution.Daily))
self.numdays = 30
self.ceiling,self.floor = 60,20
self.SetBenchmark("SPY")
self.aapl = self.AddEquity("AAPL", Resolution.Daily).Symbol
self.nvda = self.AddEquity("NVDA", Resolution.Daily).Symbol
self.cache = {
self.aapl: SymbolData(self.aapl, self),
self.nvda: SymbolData(self.nvda, self)
}
def SetSignal(self):
close = self.History(self.syl, 31, Resolution.Daily)['close']
todayvol = np.std(close[1:31])
yesterdayvol = np.std(close[0:30])
deltavol = (todayvol - yesterdayvol) / todayvol
self.numdays = int(round(self.numdays * (1 + deltavol)))
if self.numdays > self.ceiling:
self.numdays = self.ceiling
elif self.numdays < self.floor:
self.numdays = self.floor
self.high = self.History(self.syl, self.numdays, Resolution.Daily)['high']
self.low = self.History(self.syl, self.numdays, Resolution.Daily)['low']
self.buypoint = max(self.high)
self.sellpoint = min(self.low)
historyclose = self.History(self.syl, self.numdays, Resolution.Daily)['close']
self.longLiqPoint = np.mean(historyclose)
self.shortLiqPoint = np.mean(historyclose)
self.yesterdayclose = historyclose.iloc[-1]
# wait for our BollingerBand to fully initialize
if not self.Bolband.IsReady: return
holdings = self.Portfolio[self.syl].Quantity
if self.yesterdayclose > self.Bolband.UpperBand.Current.Value and self.Portfolio[self.syl].Price >= self.buypoint:
self.SetHoldings(self.syl, 1)
elif self.yesterdayclose < self.Bolband.LowerBand.Current.Value and self.Portfolio[self.syl].Price <= self.sellpoint:
self.SetHoldings(self.syl, -1)
if holdings > 0 and self.Portfolio[self.syl].Price <= self.shortLiqPoint:
self.Liquidate(self.syl)
elif holdings < 0 and self.Portfolio[self.syl].Price >= self.shortLiqPoint:
self.Liquidate(self.syl)
self.Log(str(self.yesterdayclose)+(" # of days ")+(str(self.numdays)))
def OnData(self,data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# wait for our macd to fully initialize
if not self.MACD.IsReady: return
# only once per day
if self.__previous.date() == self.Time.date(): return
# define a small tolerance on our checks to avoid bouncing
tolerance = 0.0025
holdings = self.Portfolio[self.cache].Quantity
signalDeltaPercent = (self.MACD.Current.Value - self.MACD.Signal.Current.Value)/self.MACD.Fast.Current.Value
# if our macd is greater than our signal, then let's go long
if holdings <= 0 and signalDeltaPercent > tolerance: # 0.01%
# longterm says buy as well
self.SetHoldings("AAPL", 1.0)
self.SetHoldings("NVDA", 1.0)
# of our macd is less than our signal, then let's go short
elif holdings >= 0 and signalDeltaPercent < -tolerance:
self.Liquidate("AAPL")
self.Liquidate("NVDA")
self.__previous = self.Timefrom datetime import datetime
import decimal
import numpy as np
class DynamicBreakoutAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018,1,1)
self.SetEndDate(datetime.now())
self.SetCash(100000)
Equity = self.AddEquity("NVDA", Resolution.Daily)
self.syl = Equity.Symbol
self.Schedule.On(self.DateRules.EveryDay(self.syl), self.TimeRules.BeforeMarketClose(self.syl,1),Action(self.SetSignal))
self.numdays = 20
self.ceiling,self.floor = 60,20
self.buypoint, self.sellpoint= None, None
self.longLiqPoint, self.shortLiqPoint, self.yesterdayclose= None, None, None
self.SetBenchmark(self.syl)
self.Bolband = self.BB(self.syl, self.numdays, 2, MovingAverageType.Simple, Resolution.Daily)
self.__macd = self.MACD("NVDA", 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
self.__previous = datetime.min
self.PlotIndicator("MACD", True, self.__macd, self.__macd.Signal)
self.PlotIndicator("NVDA", self.__macd.Fast, self.__macd.Slow)
def SetSignal(self):
close = self.History(self.syl, 31, Resolution.Daily)['close']
todayvol = np.std(close[1:31])
yesterdayvol = np.std(close[0:30])
deltavol = (todayvol - yesterdayvol) / todayvol
self.numdays = int(round(self.numdays * (1 + deltavol)))
if self.numdays > self.ceiling:
self.numdays = self.ceiling
elif self.numdays < self.floor:
self.numdays = self.floor
self.high = self.History(self.syl, self.numdays, Resolution.Daily)['high']
self.low = self.History(self.syl, self.numdays, Resolution.Daily)['low']
self.buypoint = max(self.high)
self.sellpoint = min(self.low)
historyclose = self.History(self.syl, self.numdays, Resolution.Daily)['close']
self.longLiqPoint = np.mean(historyclose)
self.shortLiqPoint = np.mean(historyclose)
self.yesterdayclose = historyclose.iloc[-1]
# wait for our BollingerBand to fully initialize
if not self.Bolband.IsReady: return
holdings = self.Portfolio[self.syl].Quantity
if self.yesterdayclose > self.Bolband.UpperBand.Current.Value and self.Portfolio[self.syl].Price >= self.buypoint:
self.SetHoldings(self.syl, 1)
elif self.yesterdayclose < self.Bolband.LowerBand.Current.Value and self.Portfolio[self.syl].Price <= self.sellpoint:
self.SetHoldings(self.syl, -1)
if holdings > 0 and self.Portfolio[self.syl].Price <= self.shortLiqPoint:
self.Liquidate(self.syl)
elif holdings < 0 and self.Portfolio[self.syl].Price >= self.shortLiqPoint:
self.Liquidate(self.syl)
self.Log(str(self.yesterdayclose)+(" # of days ")+(str(self.numdays)))
def OnData(self,data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# wait for our macd to fully initialize
if not self.__macd.IsReady: return
# only once per day
if self.__previous.date() == self.Time.date(): return
# define a small tolerance on our checks to avoid bouncing
tolerance = 0.0025
holdings = self.Portfolio["NVDA"].Quantity
signalDeltaPercent = (self.__macd.Current.Value - self.__macd.Signal.Current.Value)/self.__macd.Fast.Current.Value
# if our macd is greater than our signal, then let's go long
if holdings <= 0 and signalDeltaPercent > tolerance: # 0.01%
# longterm says buy as well
self.SetHoldings("NVDA", 1.0)
# of our macd is less than our signal, then let's go short
elif holdings >= 0 and signalDeltaPercent < -tolerance:
self.Liquidate("NVDA")
self.__previous = self.Timefrom datetime import datetime
import decimal
import numpy as np
class DynamicBreakoutAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018,1,1)
self.SetEndDate(datetime.now())
self.SetCash(100000)
Equity = self.AddEquity("AAPL", Resolution.Daily)
self.syl = Equity.Symbol
self.Schedule.On(self.DateRules.EveryDay(self.syl), self.TimeRules.BeforeMarketClose(self.syl,1),Action(self.SetSignal))
self.numdays = 20
self.ceiling,self.floor = 60,20
self.buypoint, self.sellpoint= None, None
self.longLiqPoint, self.shortLiqPoint, self.yesterdayclose= None, None, None
self.SetBenchmark(self.syl)
self.Bolband = self.BB(self.syl, self.numdays, 2, MovingAverageType.Simple, Resolution.Daily)
self.__macd = self.MACD("AAPL", 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
self.__previous = datetime.min
self.PlotIndicator("MACD", True, self.__macd, self.__macd.Signal)
self.PlotIndicator("AAPL", self.__macd.Fast, self.__macd.Slow)
def SetSignal(self):
close = self.History(self.syl, 31, Resolution.Daily)['close']
todayvol = np.std(close[1:31])
yesterdayvol = np.std(close[0:30])
deltavol = (todayvol - yesterdayvol) / todayvol
self.numdays = int(round(self.numdays * (1 + deltavol)))
if self.numdays > self.ceiling:
self.numdays = self.ceiling
elif self.numdays < self.floor:
self.numdays = self.floor
self.high = self.History(self.syl, self.numdays, Resolution.Daily)['high']
self.low = self.History(self.syl, self.numdays, Resolution.Daily)['low']
self.buypoint = max(self.high)
self.sellpoint = min(self.low)
historyclose = self.History(self.syl, self.numdays, Resolution.Daily)['close']
self.longLiqPoint = np.mean(historyclose)
self.shortLiqPoint = np.mean(historyclose)
self.yesterdayclose = historyclose.iloc[-1]
# wait for our BollingerBand to fully initialize
if not self.Bolband.IsReady: return
holdings = self.Portfolio[self.syl].Quantity
if self.yesterdayclose > self.Bolband.UpperBand.Current.Value and self.Portfolio[self.syl].Price >= self.buypoint:
self.SetHoldings(self.syl, 1)
elif self.yesterdayclose < self.Bolband.LowerBand.Current.Value and self.Portfolio[self.syl].Price <= self.sellpoint:
self.SetHoldings(self.syl, -1)
if holdings > 0 and self.Portfolio[self.syl].Price <= self.shortLiqPoint:
self.Liquidate(self.syl)
elif holdings < 0 and self.Portfolio[self.syl].Price >= self.shortLiqPoint:
self.Liquidate(self.syl)
self.Log(str(self.yesterdayclose)+(" # of days ")+(str(self.numdays)))
def OnData(self,data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# wait for our macd to fully initialize
if not self.__macd.IsReady: return
# only once per day
if self.__previous.date() == self.Time.date(): return
# define a small tolerance on our checks to avoid bouncing
tolerance = 0.0025
holdings = self.Portfolio["AAPL"].Quantity
signalDeltaPercent = (self.__macd.Current.Value - self.__macd.Signal.Current.Value)/self.__macd.Fast.Current.Value
# if our macd is greater than our signal, then let's go long
if holdings <= 0 and signalDeltaPercent > tolerance: # 0.01%
# longterm says buy as well
self.SetHoldings("AAPL", 1.0)
# of our macd is less than our signal, then let's go short
elif holdings >= 0 and signalDeltaPercent < -tolerance:
self.Liquidate("AAPL")
self.__previous = self.Time
