| Overall Statistics |
|
Total Trades 14 Average Win 0.70% Average Loss -0.22% Compounding Annual Return 3.088% Drawdown 4.700% Expectancy 2.013 Net Profit 3.088% Sharpe Ratio 0.367 Probabilistic Sharpe Ratio 22.263% Loss Rate 29% Win Rate 71% Profit-Loss Ratio 3.22 Alpha 0.028 Beta 0.19 Annual Standard Deviation 0.064 Annual Variance 0.004 Information Ratio 0.354 Tracking Error 0.128 Treynor Ratio 0.123 Total Fees $14.00 Estimated Strategy Capacity $21000000.00 Lowest Capacity Asset PEP R735QTJ8XC9X Portfolio Turnover 0.56% |
#region imports
from AlgorithmImports import *
#endregion
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 = 0.005
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
#region imports
from AlgorithmImports import *
#endregion
## 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(2018, 1, 1)
self.SetEndDate(2019, 1, 1)
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(0.05))
#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"]
self.XLREsector = ["AMT","PLD","CCI","EQIX"]
#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.XLP].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, .2, False, "Buy Signal")
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, .2, 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)
#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, .2, 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, .2, 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, .15, 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, .15, 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, .15, 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.XLUsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
self.SetHoldings(symbol, .2, False, "Buy Signal")
#XLK
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, .2, 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, .2, False, "Buy Signal")
#self.SetHoldings(self.XLV, .5)
for symbol, symbolData in self.XLCsector_symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma50.Current.Value) :
self.SetHoldings(symbol, .2, 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