from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Algorithm.Framework")
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Portfolio import PortfolioTarget
from QuantConnect.Algorithm.Framework.Risk import RiskManagementModel
class TrailingStopRiskManagementModel(RiskManagementModel):
'''Provides an implementation of IRiskManagementModel that limits the maximum possible loss
measured from the highest unrealized profit'''
def __init__(self, maximumDrawdownPercent = 0.08):
'''Initializes a new instance of the TrailingStopRiskManagementModel class
Args:
maximumDrawdownPercent: The maximum percentage drawdown allowed for algorithm portfolio compared with the highest unrealized profit, defaults to 5% drawdown'''
self.maximumDrawdownPercent = -abs(maximumDrawdownPercent)
self.trailingHighs = dict()
self.lastDay = -1
self.percentGain = 0.01
def ManageRisk(self, algorithm, targets):
'''Manages the algorithm's risk at each time step
Args:
algorithm: The algorithm instance
targets: The current portfolio targets to be assessed for risk'''
if algorithm.Time.day == self.lastDay:
return []
self.lastDay = algorithm.Time.day
riskAdjustedTargets = list()
for kvp in algorithm.Securities:
symbol = kvp.Key
security = kvp.Value
percentChange = algorithm.Securities[symbol].Holdings.UnrealizedProfitPercent / 0.01
# Add newly invested securities
if symbol not in self.trailingHighs:
self.trailingHighs[symbol] = security.Close # Set to average holding cost
continue
# Remove if not invested
if not security.Invested and symbol in self.trailingHighs:
try:
self.trailingHighs.pop(symbol, None)
except:
continue
continue
if percentChange.is_integer() and percentChange > 0:
self.trailingHighs[symbol] = security.Close
# Check for new highs and update - set to tradebar high
# if self.trailingHighs[symbol] < security.High:
# self.trailingHighs[symbol] = security.High
# continue
# Check for securities past the drawdown limit
securityHigh = self.trailingHighs[symbol]
if securityHigh == 0:
riskAdjustedTargets.append(PortfolioTarget(symbol, 0))
continue
drawdown = (security.Low / securityHigh) - 1
if drawdown < self.maximumDrawdownPercent:
# liquidate
riskAdjustedTargets.append(PortfolioTarget(symbol, 0))
return riskAdjustedTargets## SIMON LesFlex June 2021 ##
## Modified by Vladimir
from QuantConnect.Python import PythonQuandl
### Simon LesFlex June 2021 ###
### Key Short—Term Economic Indicators. The Key Economic Indicators (KEI) database contains monthly and quarterly statistics
### (and associated statistical methodological information) for the 33 OECD member and for a selection of non—member countries
### on a wide variety of economic indicators, namely: quarterly national accounts, industrial production, composite leading indicators,
### business tendency and consumer opinion surveys, retail trade, consumer and producer prices, hourly earnings, employment/unemployment,
### interest rates, monetary aggregates, exchange rates, international trade and balance of payments. Indicators have been prepared by national statistical
### agencies primarily to meet the requirements of users within their own country. In most instances, the indicators are compiled in accordance with
### international statistical guidelines and recommendations. However, national practices may depart from these guidelines, and these departures may
### impact on comparability between countries. There is an on—going process of review and revision of the contents of the database in order to maximise
### the relevance of the database for short—term economic analysis.
### For more information see: http://stats.oecd.org/OECDStat_Metadata/ShowMetadata.ashx?Dataset=KEI&Lang=en
### Reference Data Set: https://www.quandl.com/data/OECD/KEI_LOLITOAA_OECDE_ST_M-Leading-indicator-amplitude-adjusted-OECD-Europe-Level-ratio-or-index-Monthly
## keihist = 1400
import numpy as np
from itertools import groupby
class QuandlImporterAlgorithm(QCAlgorithm):
def Initialize(self):
self.quandlCode = "OECD/KEI_LOLITOAA_OECDE_ST_M"
## Optional argument - personal token necessary for restricted dataset
#Quandl.SetAuthCode("PrzwuZR28Wqegvv1sdJ7")
self.SetBrokerageModel(BrokerageName.AlphaStreams)
self.SetStartDate(2015,1,1) #Set Start Date
self.SetEndDate(2015,6,1) #Set End Date
self.SetCash(25000) #Set Strategy Cash
self.SetWarmup(100)
self.SetBenchmark("SPY")
self.init = True
self.kei = self.AddData(QuandlCustomColumns, self.quandlCode, Resolution.Daily, TimeZones.NewYork).Symbol
self.sma = self.SMA(self.kei, 1)
self.mom = self.MOMP(self.kei, 2)
self.XLFsector_symbolDataBySymbol = {}
self.XLEsector_symbolDataBySymbol = {}
self.XLBsector_symbolDataBySymbol = {}
self.XLIsector_symbolDataBySymbol = {}
self.XLYsector_symbolDataBySymbol = {}
self.XLPsector_symbolDataBySymbol = {}
self.XLUsector_symbolDataBySymbol = {}
self.XLKsector_symbolDataBySymbol = {}
self.XLVsector_symbolDataBySymbol = {}
self.XLCsector_symbolDataBySymbol = {}
self.AddRiskManagement(TrailingStopRiskManagementModel(0.06))
#self.SPY = self.AddEquity('SPY', Resolution.Daily).Symbol
self.stock = self.AddEquity('QQQ', Resolution.Hour).Symbol
self.bond = self.AddEquity('TLT', Resolution.Hour).Symbol
self.vix = self.AddEquity('VIX', Resolution.Minute).Symbol
self.XLF = self.AddEquity('XLF', Resolution.Hour).Symbol
self.XLE = self.AddEquity('XLE', Resolution.Hour).Symbol
self.XLB = self.AddEquity('XLB', Resolution.Hour).Symbol
self.XLI = self.AddEquity('XLI', Resolution.Hour).Symbol
self.XLY = self.AddEquity('XLY', Resolution.Hour).Symbol
self.XLP = self.AddEquity('XLP', Resolution.Hour).Symbol
self.XLU = self.AddEquity('XLU', Resolution.Hour).Symbol
self.XLK = self.AddEquity('XLK', Resolution.Hour).Symbol
self.XLV = self.AddEquity('XLV', Resolution.Hour).Symbol
self.XLC = self.AddEquity('XLC', Resolution.Hour).Symbol
#Stocks in Sectors
#self.XLFsector = ["JPM","BAC","BRK.B"]
self.XLFsector = []
#self.XLEsector = ["XOM","CVX"]
self.XLEsector = []
self.XLBsector = ["LIN","SHW","APD"]
self.XLIsector = ["HON","UNP","UPS"]
self.XLYsector = ["AMZN","TSLA","HD"]
self.XLPsector = ["PG","KO","PEP","WMT"]
self.XLUsector = ["NEE","DUK","SO"]
self.XLKsector = ["APPL","MSFT","NVDA"]
self.XLVsector = ["JNJ","PFE","UNH"]
self.XLCsector = ["FB", "GOOG", "DIS"]
#Strategy
strat = "self.Securities[symbol].Close < symbolData.high.Current.Value"
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.XLFcoarse, self.XLFfine)
#self.AddUniverse(self.XLEcoarse, self.XLEfine)
self.numberOfSymbolsCoarse = 500
self.lastMonth = 0
self.weight = 0
self.activelyTrading = []
# symbol_list = ['XLC', 'XLE', 'XLU', 'XLI', 'XLB', 'XLK', 'XLP', 'XLY', 'XLF', 'XLV']
#self.symbols = [self.AddEquity(symbol, Resolution.Minute).Symbol for symbol in symbol_list]
for symbol in self.XLFsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLFsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLEsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLEsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLBsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLBsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLIsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLIsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLYsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLYsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLPsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLPsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLUsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLUsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLKsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLKsector_symbolDataBySymbol[symbol] = symbolData
self.Schedule.On(self.DateRules.EveryDay(self.stock), self.TimeRules.AfterMarketOpen(self.stock, 1),
self.Rebalance)
#self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.Every(30), self.EveryDayAfterMarketOpen)
def XLFcoarse(self, coarse):
if self.Time.day == self.lastMonth:
return Universe.Unchanged
self.lastMonth = self.Time.day
allCoarse = [x for x in coarse if x.HasFundamentalData and x.Price > 50 and x.Volume > 200000]
finalCoarse = sorted(allCoarse, key = lambda x: x.DollarVolume, reverse = True)
return [x.Symbol for x in finalCoarse][:self.numberOfSymbolsCoarse]
#return self.tickers
def XLFfine(self, fine):
filteredSymbols = []
sortedBySector = [x for x in fine]
for code, g in groupby(sortedBySector, lambda x: x.AssetClassification.MorningstarSectorCode):
for x in sorted(g, key = lambda x: x.ValuationRatios.PERatio, reverse = False)[:2]:
filteredSymbols.append(x.Symbol)
self.XLFsector = filteredSymbols
return filteredSymbols[:3]
def XLEcoarse(self, coarse):
if self.Time.day == self.lastMonth:
return Universe.Unchanged
self.lastMonth = self.Time.day
allCoarse = [x for x in coarse if x.HasFundamentalData and x.Price > 50 and x.Volume > 200000]
finalCoarse = sorted(allCoarse, key = lambda x: x.DollarVolume, reverse = True)
return [x.Symbol for x in finalCoarse][:self.numberOfSymbolsCoarse]
#return self.tickers
def XLEfine(self, fine):
filteredSymbols = []
sortedBySector = [x for x in fine]
for code, g in groupby(sortedBySector, lambda x: x.AssetClassification.MorningstarSectorCode):
for x in sorted(g, key = lambda x: x.ValuationRatios.PERatio, reverse = False)[:2]:
filteredSymbols.append(x.Symbol)
self.XLEsector = filteredSymbols
return filteredSymbols[:3]
def OnSecuritiesChanged(self,changes):
for x in changes.AddedSecurities:
if x.Symbol not in self.XLFsector_symbolDataBySymbol:
self.XLFsector_symbolDataBySymbol = SymbolData(self,x.Symbol)
for x in changes.RemovedSecurities:
symbol_data = self.XLFsector_symbolDataBySymbol.pop(x.Symbol, None)
if symbol_data:
symbol_data.dispose()
def Rebalance(self):
if self.IsWarmingUp or not self.mom.IsReady or not self.sma.IsReady: return
initial_asset = self.stock if self.mom.Current.Value > 0 else self.bond
if self.init:
#self.SetHoldings(initial_asset, 1)
self.init = False
keihist = self.History([self.kei], (int(self.GetParameter("keihist"))))
#keihist = keihist['Value'].unstack(level=0).dropna()
keihistlowt = np.nanpercentile(keihist, 15)
keihistmidt = np.nanpercentile(keihist, 50)
keihisthight = np.nanpercentile(keihist, 90)
kei = self.sma.Current.Value
keimom = self.mom.Current.Value
if (keimom < 0 and kei < keihistmidt and kei > keihistlowt) and not (self.Securities[self.bond].Invested):
# DECLINE
self.Liquidate()
self.SetHoldings(self.XLP, .01)
for symbol, symbolData in self.XLPsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.SetHoldings(self.bond, .5)
self.Debug("STAPLES {0} >> {1}".format(self.XLP, self.Time))
elif (keimom > 0 and kei < keihistlowt) and not (self.Securities[self.XLB].Invested):
# RECOVERY
self.Liquidate()
self.SetHoldings(self.XLB, .01)
for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLB
for symbol, symbolData in self.XLBsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLY
for symbol, symbolData in self.XLYsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.Debug("MATERIALS {0} >> {1}".format(self.XLB, self.Time))
elif (keimom > 0 and kei > keihistlowt and kei < keihistmidt) and not (self.Securities[self.XLE].Invested):
# EARLY
self.Liquidate()
self.SetHoldings(self.XLE, .01)
#XLF
for symbol, symbolData in self.XLFsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLI
for symbol, symbolData in self.XLIsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLE
for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.Debug("ENERGY {0} >> {1}".format(self.XLE, self.Time))
elif (keimom > 0 and kei > keihistmidt and kei < keihisthight) and not (self.Securities[self.XLU].Invested):
# REBOUND
self.Liquidate()
#self.SetHoldings(self.XLK, .5)
self.SetHoldings(self.XLU, .01)
#XLU
for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.Debug("UTILITIES {0} >> {1}".format(self.XLU, self.Time))
elif (keimom < 0 and kei < keihisthight and kei > keihistmidt) and not (self.Securities[self.XLK].Invested):
# LATE
self.Liquidate()
self.SetHoldings(self.XLK, .01)
for symbol, symbolData in self.XLKsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value) :
self.SetHoldings(symbol, .1, False, "Buy Signal")
#self.SetHoldings(self.XLV, .5)
for symbol, symbolData in self.XLVsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value) :
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.Debug("INFO TECH {0} >> {1}".format(self.XLK, self.Time))
elif (keimom < 0 and kei < 100 and not self.Securities[self.bond].Invested):
self.Liquidate()
self.SetHoldings(self.bond, 1)
self.Plot("LeadInd", "SMA(LeadInd)", self.sma.Current.Value)
self.Plot("LeadInd", "THRESHOLD", 100)
self.Plot("MOMP", "MOMP(LeadInd)", self.mom.Current.Value)
self.Plot("MOMP", "THRESHOLD", 0)
# Quandl often doesn't use close columns so need to tell LEAN which is the "value" column.
class QuandlCustomColumns(PythonQuandl):
def __init__(self):
# Define ValueColumnName: cannot be None, Empty or non-existant column name
self.ValueColumnName = "Value"
class SymbolData:
def __init__(self, symbol, sma7, ema10, sma20, sma50, sma200, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow):
self.Symbol = symbol
self.sma7 = sma7
self.ema10 = ema10
self.sma20 = sma20
self.sma50 = sma50
self.sma200 = sma200
self.ema20 = ema20
self.rsi = rsi
self.wilr = wilr
self.wilr_fast = wilr_fast
self.high = high
self.midhigh = midhigh
self.low = low
self.stoplow = stoplow## SIMON LesFlex June 2021 ##
## Modified by Vladimir
from QuantConnect.Python import PythonQuandl
### Simon LesFlex June 2021 ###
### Key Short—Term Economic Indicators. The Key Economic Indicators (KEI) database contains monthly and quarterly statistics
### (and associated statistical methodological information) for the 33 OECD member and for a selection of non—member countries
### on a wide variety of economic indicators, namely: quarterly national accounts, industrial production, composite leading indicators,
### business tendency and consumer opinion surveys, retail trade, consumer and producer prices, hourly earnings, employment/unemployment,
### interest rates, monetary aggregates, exchange rates, international trade and balance of payments. Indicators have been prepared by national statistical
### agencies primarily to meet the requirements of users within their own country. In most instances, the indicators are compiled in accordance with
### international statistical guidelines and recommendations. However, national practices may depart from these guidelines, and these departures may
### impact on comparability between countries. There is an on—going process of review and revision of the contents of the database in order to maximise
### the relevance of the database for short—term economic analysis.
### For more information see: http://stats.oecd.org/OECDStat_Metadata/ShowMetadata.ashx?Dataset=KEI&Lang=en
### Reference Data Set: https://www.quandl.com/data/OECD/KEI_LOLITOAA_OECDE_ST_M-Leading-indicator-amplitude-adjusted-OECD-Europe-Level-ratio-or-index-Monthly
## keihist = 1400
import numpy as np
class QuandlImporterAlgorithm(QCAlgorithm):
def Initialize(self):
self.quandlCode = "OECD/KEI_LOLITOAA_OECDE_ST_M"
## Optional argument - personal token necessary for restricted dataset
#Quandl.SetAuthCode("PrzwuZR28Wqegvv1sdJ7")
self.SetBrokerageModel(BrokerageName.AlphaStreams)
self.SetStartDate(2015,1,1) #Set Start Date
#self.SetEndDate(2021,1,1) #Set End Date
self.SetCash(25000) #Set Strategy Cash
self.SetWarmup(100)
self.SetBenchmark("SPY")
self.init = True
self.kei = self.AddData(QuandlCustomColumns, self.quandlCode, Resolution.Daily, TimeZones.NewYork).Symbol
self.sma = self.SMA(self.kei, 1)
self.mom = self.MOMP(self.kei, 2)
self.XLFsector_symbolDataBySymbol = {}
self.XLEsector_symbolDataBySymbol = {}
self.XLBsector_symbolDataBySymbol = {}
self.XLIsector_symbolDataBySymbol = {}
self.XLYsector_symbolDataBySymbol = {}
self.XLPsector_symbolDataBySymbol = {}
self.XLUsector_symbolDataBySymbol = {}
self.XLKsector_symbolDataBySymbol = {}
self.XLVsector_symbolDataBySymbol = {}
self.XLCsector_symbolDataBySymbol = {}
self.AddRiskManagement(TrailingStopRiskManagementModel(0.06))
#self.SPY = self.AddEquity('SPY', Resolution.Daily).Symbol
self.stock = self.AddEquity('QQQ', Resolution.Hour).Symbol
self.bond = self.AddEquity('TLT', Resolution.Hour).Symbol
self.vix = self.AddEquity('VIX', Resolution.Minute).Symbol
self.XLF = self.AddEquity('XLF', Resolution.Hour).Symbol
self.XLE = self.AddEquity('XLE', Resolution.Hour).Symbol
self.XLB = self.AddEquity('XLB', Resolution.Hour).Symbol
self.XLI = self.AddEquity('XLI', Resolution.Hour).Symbol
self.XLY = self.AddEquity('XLY', Resolution.Hour).Symbol
self.XLP = self.AddEquity('XLP', Resolution.Hour).Symbol
self.XLU = self.AddEquity('XLU', Resolution.Hour).Symbol
self.XLK = self.AddEquity('XLK', Resolution.Hour).Symbol
self.XLV = self.AddEquity('XLV', Resolution.Hour).Symbol
self.XLC = self.AddEquity('XLC', Resolution.Hour).Symbol
#Stocks in Sectors
self.XLFsector = ["JPM","BAC","BRK.B"]
self.XLEsector = ["XOM","CVX"]
self.XLBsector = ["LIN","SHW","APD"]
self.XLIsector = ["HON","UNP","UPS"]
self.XLYsector = ["AMZN","TSLA","HD"]
self.XLPsector = ["PG","KO","PEP","WMT"]
self.XLUsector = ["NEE","DUK","SO"]
self.XLKsector = ["APPL","MSFT","NVDA"]
self.XLVsector = ["JNJ","PFE","UNH"]
self.XLCsector = ["FB", "GOOG", "DIS"]
#Strategy
strat = "self.Securities[symbol].Close < symbolData.high.Current.Value"
# symbol_list = ['XLC', 'XLE', 'XLU', 'XLI', 'XLB', 'XLK', 'XLP', 'XLY', 'XLF', 'XLV']
#self.symbols = [self.AddEquity(symbol, Resolution.Minute).Symbol for symbol in symbol_list]
for symbol in self.XLFsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLFsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLEsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLEsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLBsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLBsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLIsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLIsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLYsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLYsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLPsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLPsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLUsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLUsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLKsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLKsector_symbolDataBySymbol[symbol] = symbolData
self.Schedule.On(self.DateRules.EveryDay(self.stock), self.TimeRules.AfterMarketOpen(self.stock, 1),
self.Rebalance)
#self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.Every(30), self.EveryDayAfterMarketOpen)
def Rebalance(self):
if self.IsWarmingUp or not self.mom.IsReady or not self.sma.IsReady: return
initial_asset = self.stock if self.mom.Current.Value > 0 else self.bond
if self.init:
#self.SetHoldings(initial_asset, 1)
self.init = False
keihist = self.History([self.kei], (int(self.GetParameter("keihist"))))
#keihist = keihist['Value'].unstack(level=0).dropna()
keihistlowt = np.nanpercentile(keihist, 15)
keihistmidt = np.nanpercentile(keihist, 50)
keihisthight = np.nanpercentile(keihist, 90)
kei = self.sma.Current.Value
keimom = self.mom.Current.Value
if (keimom < 0 and kei < keihistmidt and kei > keihistlowt) and not (self.Securities[self.bond].Invested):
# DECLINE
self.Liquidate()
self.SetHoldings(self.XLP, .01)
for symbol, symbolData in self.XLPsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.SetHoldings(self.bond, .5)
self.Debug("STAPLES {0} >> {1}".format(self.XLP, self.Time))
elif (keimom > 0 and kei < keihistlowt) and not (self.Securities[self.XLB].Invested):
# RECOVERY
self.Liquidate()
self.SetHoldings(self.XLB, .01)
for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLB
for symbol, symbolData in self.XLBsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLY
for symbol, symbolData in self.XLYsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.Debug("MATERIALS {0} >> {1}".format(self.XLB, self.Time))
elif (keimom > 0 and kei > keihistlowt and kei < keihistmidt) and not (self.Securities[self.XLE].Invested):
# EARLY
self.Liquidate()
self.SetHoldings(self.XLE, .01)
#XLF
for symbol, symbolData in self.XLFsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLI
for symbol, symbolData in self.XLIsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLE
for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.Debug("ENERGY {0} >> {1}".format(self.XLE, self.Time))
elif (keimom > 0 and kei > keihistmidt and kei < keihisthight) and not (self.Securities[self.XLU].Invested):
# REBOUND
self.Liquidate()
#self.SetHoldings(self.XLK, .5)
self.SetHoldings(self.XLU, .01)
#XLU
for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.Debug("UTILITIES {0} >> {1}".format(self.XLU, self.Time))
elif (keimom < 0 and kei < keihisthight and kei > keihistmidt) and not (self.Securities[self.XLK].Invested):
# LATE
self.Liquidate()
self.SetHoldings(self.XLK, .01)
for symbol, symbolData in self.XLKsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value) :
self.SetHoldings(symbol, .1, False, "Buy Signal")
#self.SetHoldings(self.XLV, .5)
for symbol, symbolData in self.XLVsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value) :
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.Debug("INFO TECH {0} >> {1}".format(self.XLK, self.Time))
elif (keimom < 0 and kei < 100 and not self.Securities[self.bond].Invested):
self.Liquidate()
self.SetHoldings(self.bond, 1)
self.Plot("LeadInd", "SMA(LeadInd)", self.sma.Current.Value)
self.Plot("LeadInd", "THRESHOLD", 100)
self.Plot("MOMP", "MOMP(LeadInd)", self.mom.Current.Value)
self.Plot("MOMP", "THRESHOLD", 0)
# Quandl often doesn't use close columns so need to tell LEAN which is the "value" column.
class QuandlCustomColumns(PythonQuandl):
def __init__(self):
# Define ValueColumnName: cannot be None, Empty or non-existant column name
self.ValueColumnName = "Value"
class SymbolData:
def __init__(self, symbol, sma7, ema10, sma20, sma50, sma200, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow):
self.Symbol = symbol
self.sma7 = sma7
self.ema10 = ema10
self.sma20 = sma20
self.sma50 = sma50
self.sma200 = sma200
self.ema20 = ema20
self.rsi = rsi
self.wilr = wilr
self.wilr_fast = wilr_fast
self.high = high
self.midhigh = midhigh
self.low = low
self.stoplow = stoplow## SIMON LesFlex June 2021 ##
## Modified by Vladimir
from QuantConnect.Python import PythonQuandl
import numpy as np
import pandas as pd
import scipy as sc
from datetime import timedelta
from QuantConnect.Data.UniverseSelection import *
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
### Simon LesFlex June 2021 ###
### Key Short—Term Economic Indicators. The Key Economic Indicators (KEI) database contains monthly and quarterly statistics
### (and associated statistical methodological information) for the 33 OECD member and for a selection of non—member countries
### on a wide variety of economic indicators, namely: quarterly national accounts, industrial production, composite leading indicators,
### business tendency and consumer opinion surveys, retail trade, consumer and producer prices, hourly earnings, employment/unemployment,
### interest rates, monetary aggregates, exchange rates, international trade and balance of payments. Indicators have been prepared by national statistical
### agencies primarily to meet the requirements of users within their own country. In most instances, the indicators are compiled in accordance with
### international statistical guidelines and recommendations. However, national practices may depart from these guidelines, and these departures may
### impact on comparability between countries. There is an on—going process of review and revision of the contents of the database in order to maximise
### the relevance of the database for short—term economic analysis.
### For more information see: http://stats.oecd.org/OECDStat_Metadata/ShowMetadata.ashx?Dataset=KEI&Lang=en
### Reference Data Set: https://www.quandl.com/data/OECD/KEI_LOLITOAA_OECDE_ST_M-Leading-indicator-amplitude-adjusted-OECD-Europe-Level-ratio-or-index-Monthly
## keihist = 1400
class SectorBalancedPortfolioConstruction(QCAlgorithm):
def Initialize(self):
self.quandlCode = "OECD/KEI_LOLITOAA_OECDE_ST_M"
## Optional argument - personal token necessary for restricted dataset
#Quandl.SetAuthCode("PrzwuZR28Wqegvv1sdJ7")
self.SetBrokerageModel(BrokerageName.AlphaStreams)
self.SetStartDate(2016,8,10) #Set Start Date
#self.SetEndDate(2021,1,1) #Set End Date
self.SetCash(25000) #Set Strategy Cash
res = Resolution.Hour
self.SetWarmup(100)
self.SetBenchmark("SPY")
self.init = True
self.kei = self.AddData(QuandlCustomColumns, self.quandlCode, Resolution.Daily, TimeZones.NewYork).Symbol
self.sma = self.SMA(self.kei, 1)
self.mom = self.MOMP(self.kei, 2)
self.XLFsector_symbolDataBySymbol = {}
self.XLEsector_symbolDataBySymbol = {}
self.XLBsector_symbolDataBySymbol = {}
self.XLIsector_symbolDataBySymbol = {}
self.XLYsector_symbolDataBySymbol = {}
self.XLPsector_symbolDataBySymbol = {}
self.XLUsector_symbolDataBySymbol = {}
self.XLKsector_symbolDataBySymbol = {}
self.XLVsector_symbolDataBySymbol = {}
self.XLCsector_symbolDataBySymbol = {}
self.AddRiskManagement(TrailingStopRiskManagementModel(0.06))
self.SPY = self.AddEquity('SPY', Resolution.Daily).Symbol
self.stock = self.AddEquity('QQQ', Resolution.Hour).Symbol
self.bond = self.AddEquity('TMF', Resolution.Hour).Symbol
self.vix = self.AddEquity('VIX', Resolution.Minute).Symbol
self.XLF = self.AddEquity('XLF', Resolution.Hour).Symbol
self.XLE = self.AddEquity('XLE', Resolution.Hour).Symbol
self.XLB = self.AddEquity('XLB', Resolution.Hour).Symbol
self.XLI = self.AddEquity('XLI', Resolution.Hour).Symbol
self.XLY = self.AddEquity('XLY', Resolution.Hour).Symbol
self.XLP = self.AddEquity('XLP', Resolution.Hour).Symbol
self.XLU = self.AddEquity('XLU', Resolution.Hour).Symbol
self.XLK = self.AddEquity('XLK', Resolution.Hour).Symbol
self.XLV = self.AddEquity('XLV', Resolution.Hour).Symbol
self.XLC = self.AddEquity('XLC', Resolution.Hour).Symbol
self.XLFsector = []
#self.XLFsector = []
self.XLEsector = []
self.XLBsector = []
self.XLIsector = []
self.XLYsector = []
self.XLPsector = []
self.XLUsector = []
self.XLKsector = []
self.XLVsector = []
self.XLCsector = []
# Stock Selector
self.UniverseSettings.Resolution = Resolution.Daily
#1. Set an instance of MyUniverseSelectionModel using self.SetUniverseSelection
self.SetUniverseSelection(MyUniverseSelectionModel())
#self.AddUniverse(self.SelectCoarse,self.SelectFine)
self.activelyTrading = []
self.weight = 0
self.numberOfSymbolsCoarse = 3
self.exposureToSector = 3
self.lastMonth = -1
## Sectors data
for symbol in self.XLFsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLFsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLEsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLEsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLBsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLBsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLIsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLIsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLYsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLYsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLPsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLPsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLUsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLUsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLKsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLKsector_symbolDataBySymbol[symbol] = symbolData
## Sector Scheduler
self.Schedule.On(self.DateRules.EveryDay(self.stock), self.TimeRules.AfterMarketOpen(self.stock, 1),
self.Rebalance)
def SelectCoarse(self, algorithm, coarse):
filtered = [x for x in coarse if x.HasFundamentalData > 0 and x.Price > 0]
sortedByDollarVolume = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True)
return [x.Symbol for x in sortedByDollarVolume][:100]
def SelectFine(self, algorithm, fine):
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology]
self.XLKsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:3]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.FinancialServices]
self.XLFsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:2]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Utilities]
self.XLUsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerDefensive]
self.XLPsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerCyclical]
self.XLYsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.RealEstate]
self.XLREsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.BasicMaterials]
self.XLBsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.CommunicationServices]
self.XLCsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Energy]
self.XLEsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Industrials]
self.XLIsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Healthcare]
self.XLVsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
return [x.Symbol for x in self.XLKsector + self.XLFsector + self.XLUsector + self.XLPsector + self.XLYsector + self.XLREsector + self.XLBsector + self.XLCsector + self.XLEsector + self.XLIsector + self.XLVsector]
#self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.Every(30), self.EveryDayAfterMarketOpen)
def Rebalance(self):
if self.IsWarmingUp or not self.mom.IsReady or not self.sma.IsReady: return
initial_asset = self.stock if self.mom.Current.Value > 0 else self.bond
if self.init:
#self.SetHoldings(initial_asset, 1)
self.init = False
keihist = self.History([self.kei], (int(self.GetParameter("keihist"))))
#keihist = keihist['Value'].unstack(level=0).dropna()
keihistlowt = np.nanpercentile(keihist, 15)
keihistmidt = np.nanpercentile(keihist, 50)
keihisthight = np.nanpercentile(keihist, 90)
kei = self.sma.Current.Value
keimom = self.mom.Current.Value
if (keimom < 0 and kei < keihistmidt and kei > keihistlowt) and not (self.Securities[self.bond].Invested) and self.Portfolio["QQQ"].Invested:
# DECLINE
self.Liquidate()
self.SetHoldings(self.XLP, .01)
for symbol, symbolData in self.XLPsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#Stop Loss
# for symbol, symbolData in self.XLPsector_symbolDataBySymbol.items():
# if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLB].Invested):
# self.Liquidate()
# self.SetHoldings(self.XLP, .01)
self.SetHoldings(self.bond, .4)
self.Debug("STAPLES {0} >> {1}".format(self.XLP, self.Time))
elif (keimom > 0 and kei < keihistlowt) and not (self.Securities[self.XLB].Invested) and self.Portfolio["QQQ"].Invested:
# RECOVERY
self.Liquidate()
self.SetHoldings(self.XLB, .01)
for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLB
for symbol, symbolData in self.XLBsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLY
for symbol, symbolData in self.XLYsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#Stop Loss
# for symbol, symbolData in self.XLBsector_symbolDataBySymbol.items():
# if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLB].Invested):
# self.Liquidate()
# self.SetHoldings(self.XLB, .01)
self.Debug("MATERIALS {0} >> {1}".format(self.XLB, self.Time))
elif (keimom > 0 and kei > keihistlowt and kei < keihistmidt) and not (self.Securities[self.XLE].Invested) and self.Portfolio["QQQ"].Invested:
# EARLY
self.Liquidate()
self.SetHoldings(self.XLE, .01)
#XLF
for symbol, symbolData in self.XLFsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLI
for symbol, symbolData in self.XLIsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLE
for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#Stop Loss
# for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
# if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLE].Invested):
# self.Liquidate()
# self.SetHoldings(self.XLE, .01)
self.Debug("ENERGY {0} >> {1}".format(self.XLE, self.Time))
elif (keimom > 0 and kei > keihistmidt and kei < keihisthight) and not (self.Securities[self.XLU].Invested) and self.Portfolio["QQQ"].Invested:
# REBOUND
self.Liquidate()
#self.SetHoldings(self.XLK, .5)
self.SetHoldings(self.XLU, .01)
#XLU
for symbol, symbolData in self.XLUsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#Stop Loss
# for symbol, symbolData in self.XLUsector_symbolDataBySymbol.items():
# if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLU].Invested):
# self.Liquidate()
# self.SetHoldings(self.XLU, .01)
self.Debug("UTILITIES {0} >> {1}".format(self.XLU, self.Time))
elif (keimom < 0 and kei < keihisthight and kei > keihistmidt) and not (self.Securities[self.XLK].Invested) and self.Portfolio["QQQ"].Invested:
# LATE
self.Liquidate()
self.SetHoldings(self.XLK, .01)
for symbol, symbolData in self.XLKsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value) :
self.SetHoldings(symbol, .1, False, "Buy Signal")
#self.SetHoldings(self.XLV, .5)
for symbol, symbolData in self.XLVsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value) :
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.Debug("INFO TECH {0} >> {1}".format(self.XLK, self.Time))
#Stop Loss
# for symbol, symbolData in self.XLKsector_symbolDataBySymbol.items():
# if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLK].Invested):
# self.Liquidate()
# self.SetHoldings(self.XLK, .01)
elif (keimom < 0 and kei < 100 and not self.Securities[self.bond].Invested):
self.Liquidate()
self.SetHoldings(self.bond, .5)
self.Plot("LeadInd", "SMA(LeadInd)", self.sma.Current.Value)
self.Plot("LeadInd", "THRESHOLD", 100)
self.Plot("MOMP", "MOMP(LeadInd)", self.mom.Current.Value)
self.Plot("MOMP", "THRESHOLD", 0)
# Quandl often doesn't use close columns so need to tell LEAN which is the "value" column.
class QuandlCustomColumns(PythonQuandl):
def __init__(self):
# Define ValueColumnName: cannot be None, Empty or non-existant column name
self.ValueColumnName = "Value"
class SymbolData:
def __init__(self, symbol, sma7, ema10, sma20, sma50, sma200, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow):
self.Symbol = symbol
self.sma7 = sma7
self.ema10 = ema10
self.sma20 = sma20
self.sma50 = sma50
self.sma200 = sma200
self.ema20 = ema20
self.rsi = rsi
self.wilr = wilr
self.wilr_fast = wilr_fast
self.high = high
self.midhigh = midhigh
self.low = low
self.stoplow = stoplow
#class MyUniverseSelectionModel(FundamentalUniverseSelectionModel):
def __init__(self):
super().__init__(True, None)
def SelectCoarse(self, algorithm, coarse):
filtered = [x for x in coarse if x.HasFundamentalData > 0 and x.Price > 0]
sortedByDollarVolume = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True)
return [x.Symbol for x in sortedByDollarVolume][:100]
def SelectFine(self, algorithm, fine):
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology]
self.XLKsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:3]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.FinancialServices]
self.XLFsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:2]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Utilities]
self.XLUsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerDefensive]
self.XLPsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerCyclical]
self.XLYsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.RealEstate]
self.XLREsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.BasicMaterials]
self.XLBsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.CommunicationServices]
self.XLCsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Energy]
self.XLEsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Industrials]
self.XLIsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Healthcare]
self.XLVsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
return [x.Symbol for x in self.XLKsector + self.XLFsector + self.XLUsector + self.XLPsector + self.XLYsector + self.XLREsector + self.XLBsector + self.XLCsector + self.XLEsector + self.XLIsector + self.XLVsector]
## SIMON LesFlex June 2021 ##
## Modified by Vladimir
from QuantConnect.Python import PythonQuandl
import numpy as np
import pandas as pd
import scipy as sc
from datetime import timedelta
from QuantConnect.Data.UniverseSelection import *
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
### Simon LesFlex June 2021 ###
### Key Short—Term Economic Indicators. The Key Economic Indicators (KEI) database contains monthly and quarterly statistics
### (and associated statistical methodological information) for the 33 OECD member and for a selection of non—member countries
### on a wide variety of economic indicators, namely: quarterly national accounts, industrial production, composite leading indicators,
### business tendency and consumer opinion surveys, retail trade, consumer and producer prices, hourly earnings, employment/unemployment,
### interest rates, monetary aggregates, exchange rates, international trade and balance of payments. Indicators have been prepared by national statistical
### agencies primarily to meet the requirements of users within their own country. In most instances, the indicators are compiled in accordance with
### international statistical guidelines and recommendations. However, national practices may depart from these guidelines, and these departures may
### impact on comparability between countries. There is an on—going process of review and revision of the contents of the database in order to maximise
### the relevance of the database for short—term economic analysis.
### For more information see: http://stats.oecd.org/OECDStat_Metadata/ShowMetadata.ashx?Dataset=KEI&Lang=en
### Reference Data Set: https://www.quandl.com/data/OECD/KEI_LOLITOAA_OECDE_ST_M-Leading-indicator-amplitude-adjusted-OECD-Europe-Level-ratio-or-index-Monthly
## keihist = 1400
class SectorBalancedPortfolioConstruction(QCAlgorithm):
def Initialize(self):
self.quandlCode = "OECD/KEI_LOLITOAA_OECDE_ST_M"
## Optional argument - personal token necessary for restricted dataset
#Quandl.SetAuthCode("PrzwuZR28Wqegvv1sdJ7")
self.SetBrokerageModel(BrokerageName.AlphaStreams)
self.SetStartDate(2016,8,10) #Set Start Date
#self.SetEndDate(2021,1,1) #Set End Date
self.SetCash(25000) #Set Strategy Cash
res = Resolution.Hour
self.SetWarmup(100)
self.SetBenchmark("SPY")
self.init = True
self.kei = self.AddData(QuandlCustomColumns, self.quandlCode, Resolution.Daily, TimeZones.NewYork).Symbol
self.sma = self.SMA(self.kei, 1)
self.mom = self.MOMP(self.kei, 2)
self.XLFsector_symbolDataBySymbol = {}
self.XLEsector_symbolDataBySymbol = {}
self.XLBsector_symbolDataBySymbol = {}
self.XLIsector_symbolDataBySymbol = {}
self.XLYsector_symbolDataBySymbol = {}
self.XLPsector_symbolDataBySymbol = {}
self.XLUsector_symbolDataBySymbol = {}
self.XLKsector_symbolDataBySymbol = {}
self.XLVsector_symbolDataBySymbol = {}
self.XLCsector_symbolDataBySymbol = {}
self.AddRiskManagement(TrailingStopRiskManagementModel(0.06))
self.SPY = self.AddEquity('SPY', Resolution.Daily).Symbol
self.stock = self.AddEquity('QQQ', Resolution.Hour).Symbol
self.bond = self.AddEquity('TMF', Resolution.Hour).Symbol
self.vix = self.AddEquity('VIX', Resolution.Minute).Symbol
self.XLF = self.AddEquity('XLF', Resolution.Hour).Symbol
self.XLE = self.AddEquity('XLE', Resolution.Hour).Symbol
self.XLB = self.AddEquity('XLB', Resolution.Hour).Symbol
self.XLI = self.AddEquity('XLI', Resolution.Hour).Symbol
self.XLY = self.AddEquity('XLY', Resolution.Hour).Symbol
self.XLP = self.AddEquity('XLP', Resolution.Hour).Symbol
self.XLU = self.AddEquity('XLU', Resolution.Hour).Symbol
self.XLK = self.AddEquity('XLK', Resolution.Hour).Symbol
self.XLV = self.AddEquity('XLV', Resolution.Hour).Symbol
self.XLC = self.AddEquity('XLC', Resolution.Hour).Symbol
self.XLFsector = []
#self.XLFsector = []
self.XLEsector = []
self.XLBsector = []
self.XLIsector = []
self.XLYsector = []
self.XLPsector = []
self.XLUsector = []
self.XLKsector = []
self.XLVsector = []
self.XLCsector = []
# Stock Selector
self.UniverseSettings.Resolution = Resolution.Daily
#1. Set an instance of MyUniverseSelectionModel using self.SetUniverseSelection
#self.SetUniverseSelection()
self.AddUniverse(self.SelectCoarse, self.SelectFine)
self.SetWarmup(500)
self.symbolBySectorCode = dict()
self.activelyTrading = []
self.weight = 0
self.numberOfSymbolsCoarse = 3
self.exposureToSector = 3
self.lastMonth = -1
## Sectors data
for symbol in self.XLFsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLFsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLEsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLEsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLBsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLBsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLIsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLIsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLYsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLYsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLPsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLPsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLUsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLUsector_symbolDataBySymbol[symbol] = symbolData
for symbol in self.XLKsector:
self.AddEquity(symbol, Resolution.Hour)
ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
wilr = self.WILR(symbol, 14, Resolution.Daily)
wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
low = self.MIN(symbol, 10, Resolution.Daily, Field.Low)
stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
self.XLKsector_symbolDataBySymbol[symbol] = symbolData
## Sector Scheduler
self.Schedule.On(self.DateRules.EveryDay(self.stock), self.TimeRules.AfterMarketOpen(self.stock, 1),
self.Rebalance)
def SelectCoarse(self, coarse):
filtered = [x for x in coarse if x.HasFundamentalData and x.Price > 0]
sortedByDollarVolume = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True)
return [x.Symbol for x in sortedByDollarVolume][:100]
def SelectFine(self, fine):
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology]
self.XLKsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:3]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.FinancialServices]
self.XLFsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:2]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Utilities]
self.XLUsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerDefensive]
self.XLPsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerCyclical]
self.XLYsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.RealEstate]
self.XLREsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.BasicMaterials]
self.XLBsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.CommunicationServices]
self.XLCsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Energy]
self.XLEsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Industrials]
self.XLIsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Healthcare]
self.XLVsector = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1]
return [x.Symbol for x in self.XLKsector + self.XLFsector + self.XLUsector + self.XLPsector + self.XLYsector + self.XLREsector + self.XLBsector + self.XLCsector + self.XLEsector + self.XLIsector + self.XLVsector]
#self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.Every(30), self.EveryDayAfterMarketOpen)
def OnSecuritiesChanged(self, changes):
for security in changes.AddedSecurities:
sectorCode = security.Fundamentals.AssetClassification.MorningstarSectorCode
if sectorCode not in self.symbolBySectorCode:
self.symbolBySectorCode[sectorCode] = list()
self.symbolBySectorCode[sectorCode].append(security.Symbol)
for security in changes.RemovedSecurities:
sectorCode = security.Fundamentals.AssetClassification.MorningstarSectorCode
if sectorCode in self.symbolBySectorCode:
symbol = security.Symbol
if symbol in self.symbolBySectorCode[sectorCode]:
self.symbolBySectorCode[sectorCode].remove(symbol)
self.OnSecuritiesChanged(changes)
def Rebalance(self):
if self.IsWarmingUp or not self.mom.IsReady or not self.sma.IsReady: return
initial_asset = self.stock if self.mom.Current.Value > 0 else self.bond
if self.init:
#self.SetHoldings(initial_asset, 1)
self.init = False
keihist = self.History([self.kei], (int(self.GetParameter("keihist"))))
#keihist = keihist['Value'].unstack(level=0).dropna()
keihistlowt = np.nanpercentile(keihist, 15)
keihistmidt = np.nanpercentile(keihist, 50)
keihisthight = np.nanpercentile(keihist, 90)
kei = self.sma.Current.Value
keimom = self.mom.Current.Value
if (keimom < 0 and kei < keihistmidt and kei > keihistlowt) and not (self.Securities[self.bond].Invested) and self.Portfolio["QQQ"].Invested:
# DECLINE
self.Liquidate()
self.SetHoldings(self.XLP, .01)
for symbol, symbolData in self.XLPsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#Stop Loss
# for symbol, symbolData in self.XLPsector_symbolDataBySymbol.items():
# if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLB].Invested):
# self.Liquidate()
# self.SetHoldings(self.XLP, .01)
self.SetHoldings(self.bond, .4)
self.Debug("STAPLES {0} >> {1}".format(self.XLP, self.Time))
elif (keimom > 0 and kei < keihistlowt) and not (self.Securities[self.XLB].Invested) and self.Portfolio["QQQ"].Invested:
# RECOVERY
self.Liquidate()
self.SetHoldings(self.XLB, .01)
for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLB
for symbol, symbolData in self.XLBsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLY
for symbol, symbolData in self.XLYsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#Stop Loss
# for symbol, symbolData in self.XLBsector_symbolDataBySymbol.items():
# if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLB].Invested):
# self.Liquidate()
# self.SetHoldings(self.XLB, .01)
self.Debug("MATERIALS {0} >> {1}".format(self.XLB, self.Time))
elif (keimom > 0 and kei > keihistlowt and kei < keihistmidt) and not (self.Securities[self.XLE].Invested) and self.Portfolio["QQQ"].Invested:
# EARLY
self.Liquidate()
self.SetHoldings(self.XLE, .01)
#XLF
for symbol, symbolData in self.XLFsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLI
for symbol, symbolData in self.XLIsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#XLE
for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#Stop Loss
# for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
# if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLE].Invested):
# self.Liquidate()
# self.SetHoldings(self.XLE, .01)
self.Debug("ENERGY {0} >> {1}".format(self.XLE, self.Time))
elif (keimom > 0 and kei > keihistmidt and kei < keihisthight) and not (self.Securities[self.XLU].Invested) and self.Portfolio["QQQ"].Invested:
# REBOUND
self.Liquidate()
#self.SetHoldings(self.XLK, .5)
self.SetHoldings(self.XLU, .01)
#XLU
for symbol, symbolData in self.XLUsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .1, False, "Buy Signal")
#Stop Loss
# for symbol, symbolData in self.XLUsector_symbolDataBySymbol.items():
# if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLU].Invested):
# self.Liquidate()
# self.SetHoldings(self.XLU, .01)
self.Debug("UTILITIES {0} >> {1}".format(self.XLU, self.Time))
elif (keimom < 0 and kei < keihisthight and kei > keihistmidt) and not (self.Securities[self.XLK].Invested) and self.Portfolio["QQQ"].Invested:
# LATE
self.Liquidate()
self.SetHoldings(self.XLK, .01)
for symbol, symbolData in self.XLKsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value) :
self.SetHoldings(symbol, .1, False, "Buy Signal")
#self.SetHoldings(self.XLV, .5)
for symbol, symbolData in self.XLVsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value) :
self.SetHoldings(symbol, .1, False, "Buy Signal")
self.Debug("INFO TECH {0} >> {1}".format(self.XLK, self.Time))
#Stop Loss
# for symbol, symbolData in self.XLKsector_symbolDataBySymbol.items():
# if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLK].Invested):
# self.Liquidate()
# self.SetHoldings(self.XLK, .01)
elif (keimom < 0 and kei < 100 and not self.Securities[self.bond].Invested):
self.Liquidate()
self.SetHoldings(self.bond, .5)
self.Plot("LeadInd", "SMA(LeadInd)", self.sma.Current.Value)
self.Plot("LeadInd", "THRESHOLD", 100)
self.Plot("MOMP", "MOMP(LeadInd)", self.mom.Current.Value)
self.Plot("MOMP", "THRESHOLD", 0)
# Quandl often doesn't use close columns so need to tell LEAN which is the "value" column.
class QuandlCustomColumns(PythonQuandl):
def __init__(self):
# Define ValueColumnName: cannot be None, Empty or non-existant column name
self.ValueColumnName = "Value"
class SymbolData:
def __init__(self, symbol, sma7, ema10, sma20, sma50, sma200, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow):
self.Symbol = symbol
self.sma7 = sma7
self.ema10 = ema10
self.sma20 = sma20
self.sma50 = sma50
self.sma200 = sma200
self.ema20 = ema20
self.rsi = rsi
self.wilr = wilr
self.wilr_fast = wilr_fast
self.high = high
self.midhigh = midhigh
self.low = low
self.stoplow = stoplow
