| Overall Statistics |
|
Total Trades 14 Average Win 7.58% Average Loss -0.72% Compounding Annual Return 273.796% Drawdown 22.100% Expectancy 6.699 Net Profit 64.285% Sharpe Ratio 4.3 Probabilistic Sharpe Ratio 77.382% Loss Rate 33% Win Rate 67% Profit-Loss Ratio 10.55 Alpha 1.452 Beta 3.788 Annual Standard Deviation 0.592 Annual Variance 0.351 Information Ratio 4.693 Tracking Error 0.481 Treynor Ratio 0.672 Total Fees $14.85 Estimated Strategy Capacity $36000.00 Lowest Capacity Asset UTSL WK7PAZG36KIT |
import numpy as np
from QuantConnect.Python import PythonQuandl
# ------------------------------------------------------------------
STK = ['QQQ']; BND = ['TLT']; VOLA = 126; BASE_RET = 85; LEV = 1.00;
PAIRS = ['SLV', 'GLD', 'XLI', 'XLU', 'DBB', 'UUP']
# ------------------------------------------------------------------
class QuandlImporterAlgorithm(QCAlgorithm):
def Initialize(self):
#self.SetEndDate(2021, 3, 4)
self.cap = 10000
self.SetCash(self.cap)
self.quandlCode = "OECD/KEI_LOLITOAA_OECDE_ST_M"
## Optional argument - personal token necessary for restricted dataset
#Quandl.SetAuthCode("PrzwuZR28Wqegvv1sdJ7")
self.SetStartDate(2021, 1, 1) #Set Start Date
#self.SetEndDate(2020,1,1) #Set End Date
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.SPY = self.AddEquity('SPY', Resolution.Daily).Symbol
self.stock = self.AddEquity('QQQ', Resolution.Hour).Symbol
self.bond = self.AddEquity('TLT', Resolution.Hour).Symbol
self.STK = self.AddEquity('QQQ', Resolution.Minute).Symbol
self.BND = self.AddEquity('TLT', Resolution.Minute).Symbol
self.ASSETS = [self.STK, self.BND]
self.SLV = self.AddEquity('SLV', Resolution.Daily).Symbol
self.GLD = self.AddEquity('GLD', Resolution.Daily).Symbol
self.XLI = self.AddEquity('XLI', Resolution.Daily).Symbol
self.XLU = self.AddEquity('XLU', Resolution.Daily).Symbol
self.DBB = self.AddEquity('DBB', Resolution.Daily).Symbol
self.UUP = self.AddEquity('UUP', Resolution.Daily).Symbol
self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol
self.pairs = [self.SLV, self.GLD, self.XLI, self.XLU, self.DBB, self.UUP]
self.symbols = ['FAS', 'ERX', 'UYM', 'DUSL', 'WANT', 'UGE', 'UTSL', 'TECL', 'CURE', 'TENG', 'XLRE']
#Leverged 3x
self.XLF = self.AddEquity('FAS', Resolution.Hour).Symbol
self.XLE = self.AddEquity('ERX', Resolution.Hour).Symbol
self.XLB = self.AddEquity('UYM', Resolution.Hour).Symbol
self.XLI = self.AddEquity('DUSL', Resolution.Hour).Symbol
self.XLY = self.AddEquity('WANT', Resolution.Hour).Symbol
self.XLP = self.AddEquity('UGE', Resolution.Hour).Symbol
self.XLU = self.AddEquity('UTSL', Resolution.Hour).Symbol
self.XLK = self.AddEquity('TECL', Resolution.Hour).Symbol
self.XLV = self.AddEquity('CURE', Resolution.Hour).Symbol
self.XLC = self.AddEquity('TENG', Resolution.Hour).Symbol
self.XLRE = self.AddEquity('XLRE', Resolution.Hour).Symbol
#self.Schedule.On(self.DateRules.WeekStart(self.stock), self.TimeRules.AfterMarketOpen(self.stock, 31),
# self.Rebalance)
self.bull = 1
self.count = 0
self.outday = 0
self.wt = {}
self.real_wt = {}
self.mkt = []
self.SetWarmUp(timedelta(350))
self.Schedule.On(self.DateRules.EveryDay(self.stock), self.TimeRules.AfterMarketOpen(self.stock, 1),
self.Rebalance)
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 100),
self.daily_check)
symbols = [self.MKT] + self.pairs
for symbol in symbols:
self.consolidator = TradeBarConsolidator(timedelta(days=1))
self.consolidator.DataConsolidated += self.consolidation_handler
self.SubscriptionManager.AddConsolidator(symbol, self.consolidator)
self.history = self.History(symbols, VOLA + 1, Resolution.Daily)
if self.history.empty or 'close' not in self.history.columns:
return
self.history = self.history['close'].unstack(level=0).dropna()
def consolidation_handler(self, sender, consolidated):
self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
self.history = self.history.iloc[-(VOLA + 1):]
def daily_check(self):
vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252)
wait_days = int(vola * BASE_RET)
period = int((1.0 - vola) * BASE_RET)
r = self.history.pct_change(period).iloc[-1]
exit = ((r[self.SLV] < r[self.GLD]) and (r[self.XLI] < r[self.XLU]) and (r[self.DBB] < r[self.UUP]))
if exit:
self.bull = False
self.outday = self.count
if self.count >= self.outday + wait_days:
self.bull = True
self.count += 1
if not self.bull:
for sec in self.ASSETS:
self.wt[sec] = LEV if sec is self.BND else 0
self.trade()
elif self.bull:
for sec in self.ASSETS:
self.wt[sec] = LEV if sec is self.STK else 0
self.trade()
def trade(self):
for sec, weight in self.wt.items():
if weight == 0 and self.Portfolio[sec].IsLong:
self.Liquidate(sec)
cond1 = weight == 0 and self.Portfolio[sec].IsLong
cond2 = weight > 0 and not self.Portfolio[sec].Invested
if cond1 or cond2:
self.SetHoldings(sec, weight)
def OnEndOfDay(self):
mkt_price = self.Securities[self.MKT].Close
self.mkt.append(mkt_price)
mkt_perf = self.mkt[-1] / self.mkt[0] * self.cap
self.Plot('Strategy Equity', 'SPY', mkt_perf)
account_leverage = self.Portfolio.TotalHoldingsValue / self.Portfolio.TotalPortfolioValue
self.Plot('Holdings', 'leverage', round(account_leverage, 1))
for sec, weight in self.wt.items():
self.real_wt[sec] = round(self.ActiveSecurities[sec].Holdings.Quantity * self.Securities[sec].Price / self.Portfolio.TotalPortfolioValue,4)
self.Plot('Holdings', self.Securities[sec].Symbol, round(self.real_wt[sec], 3))
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], 1400)
#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, .5)
self.SetHoldings(self.XLV, .5)
#self.SetHoldings(self.bond, 1)
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, .5)
self.SetHoldings(self.XLY, .5)
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, .33)
self.SetHoldings(self.XLF, .33)
self.SetHoldings(self.XLI, .33)
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, .5)
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, .5)
self.SetHoldings(self.XLC, .5)
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)
class QuandlCustomColumns(PythonQuandl):
def __init__(self):
# Define ValueColumnName: cannot be None, Empty or non-existant column name
self.ValueColumnName = "Value"