| Overall Statistics |
|
Total Trades 29 Average Win 11.33% Average Loss -1.57% Compounding Annual Return 29.002% Drawdown 15.700% Expectancy 5.470 Net Profit 236.692% Sharpe Ratio 1.393 Probabilistic Sharpe Ratio 75.963% Loss Rate 21% Win Rate 79% Profit-Loss Ratio 7.24 Alpha 0.201 Beta 0.05 Annual Standard Deviation 0.147 Annual Variance 0.022 Information Ratio 0.504 Tracking Error 0.226 Treynor Ratio 4.077 Total Fees $104.88 Estimated Strategy Capacity $240000000.00 Lowest Capacity Asset QQQ RIWIV7K5Z9LX Portfolio Turnover 1.66% |
#region imports
from AlgorithmImports import *
#endregion
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
from AlgorithmImports import *
from CPI import CPIData
# -------------------------------------------------------------------------
STK = ['QQQ']; BND = ['TLT']; VOLA = 126; BASE_RET = 85; DAY = 85 ;LEV = 1.00 #855
LEV = 1.00 #85
LEV = 1.00; #85
PAIRS = ['SLV', 'GLD', 'XLI', 'XLU', 'DBB', 'UUP'] ; res = Resolution.Daily
# -------------------------------------------------------------------------
class DualMomentumInOut(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2022,1,1)
# self.SetEndDate(2019,1,1)
self.cap = 10000 #Settare il Capitale Iniziale
self.SetCash(self.cap)
self.AddEquity('SPY', res).Symbol
self.SetBenchmark('SPY')
self.STK = self.AddEquity('SPY', res).Symbol
self.BND1 = self.AddEquity('TLT', res).Symbol
self.BND2 = self.AddEquity('UUP', res).Symbol
self.ASSETS = [self.STK, self.BND, self.BND2]
self.SLV = self.AddEquity('SLV', res).Symbol
self.GLD = self.AddEquity('GLD', res).Symbol
self.XLI = self.AddEquity('XLI', res).Symbol
self.XLU = self.AddEquity('XLU', res).Symbol
self.DBB = self.AddEquity('DBB', res).Symbol
self.UUP = self.AddEquity('UUP', res).Symbol
# self.SPY = self.AddEquity('SPY', res).Symbol
# self.TLT = self.AddEquity('TLT', res).Symbol
self.MKT = self.AddEquity('SPY', res).Symbol
self.BNCH = self.AddEquity('SPY', res).Symbol
self.pairs = [self.XLI, self.XLU, self.GLD, self.SLV, self.DBB, self.UUP] #self.TVC, self.TIP
self.bull = 1
self.count = 0
self.outday = 0
self.wt = {}
self.real_wt = {}
self.mkt = []
self.SetWarmUp(timedelta(350))
self.quandlCode = "RATEINF/INFLATION_USA"
Quandl.SetAuthCode("hcm-xaeGb6haorprzgnh")
self.cpi = self.AddData(QuandlCustomColumns, self.quandlCode, Resolution.Daily, TimeZones.NewYork).Symbol
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 100), #100), #1000), #1005 0), #100), #1000), #100100), #100), #1000), #1005 0), #100), #1000), #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):
current_inflation = self.Securities[self.kei].Price
vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252)
wait_days = int(vola * DAY)*Mwait
self.Debug('{}'.format(wait_days))
period = int((1.0 - vola) * BASE_RET)
r = self.history.pct_change(period).iloc[-1]
rGLD = round(((r[self.GLD] - r[self.SLV]) * 50), 100)
rXLU = round(((r[self.XLU] - r[self.XLI]) * 50), 100)
rUUP = round(((r[self.UUP] - r[self.DBB]) * 50), 100)
exit = (r[self.XLI] < r[self.XLU]) and (r[self.SLV] < r[self.GLD]) 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 current_inflation > 5 :
self.safe = self.BND2
else:
self.safe = self.BND1
if not self.bull:
for sec in self.ASSETS:
self.wt[sec] = LEV if sec is self.safe else 0 if sec is self.safe 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 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):
vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252)
period = int((1.0 - vola) * (BASE_RET))
r = self.history.pct_change(period).iloc[-1]
rGLD = round(((r[self.GLD] - r[self.SLV]) * 50), 100)
rXLU = round(((r[self.XLU] - r[self.XLI]) * 50), 100)
rUUP = round(((r[self.UUP] - r[self.DBB]) * 50), 100)
# rI = round(((r[self.RINF] - r[self.TLT]) * 50), 100)
self.Plot('ROC', 'GOLD/SLV', rGLD)
self.Plot('ROC', 'XLU/XLI', rXLU)
self.Plot('ROC', 'UUP/DBB', rUUP)
# self.Plot('ROC', 'RINF/TLT', rI)
vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252)
wait_days = int(vola * DAY)
self.Plot('Wait_days', 'Days', wait_days)
# mkt_price = self.Securities[self.BNCH].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))
# Crea una istanza della classe CPIData come simbolo personalizzato
class QuandlCustomColumns(PythonQuandl):
def __init__(self):
# Define ValueColumnName: cannot be None, Empty or non-existant column name
self.ValueColumnName = "Value"
#region imports
from AlgorithmImports import *
#endregion
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
from AlgorithmImports import *
# -------------------------------------------------------------------------
STK = ['QQQ']; BND = ['TLT']; VOLA = 126; BASE_RET = 85; DAY = 85 ;LEV = 1.00 #855
LEV = 1.00 #85
LEV = 1.00; #85
PAIRS = ['SLV', 'GLD', 'XLI', 'XLU', 'DBB', 'UUP'] ; res = Resolution.Daily
# -------------------------------------------------------------------------
class DualMomentumInOut(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018,7,1)
#self.SetEndDate(2021,1,1)
self.cap = 25000 #Settare il Capitale Iniziale
self.SetCash(self.cap)
self.AddEquity('SPY', res).Symbol
self.SetBenchmark('SPY')
self.STK = self.AddEquity('QQQ', res).Symbol
self.BND1 = self.AddEquity('TLT', res).Symbol
self.BND2 = self.AddEquity('UUP', res).Symbol
self.ASSETS = [self.STK, self.BND1, self.BND2]
self.SLV = self.AddEquity('SLV', res).Symbol
self.GLD = self.AddEquity('GLD', res).Symbol
self.XLI = self.AddEquity('XLI', res).Symbol
self.XLU = self.AddEquity('XLU', res).Symbol
self.DBB = self.AddEquity('DBB', res).Symbol
self.UUP = self.AddEquity('UUP', res).Symbol
# self.SPY = self.AddEquity('SPY', res).Symbol
# self.TLT = self.AddEquity('TLT', res).Symbol
self.MKT = self.AddEquity('SPY', res).Symbol
self.BNCH = self.AddEquity('SPY', res).Symbol
self.pairs = [self.XLI, self.XLU, self.GLD, self.SLV, self.DBB, self.UUP] #self.TVC, self.TIP
self.bull = 1
self.count = 0
self.outday = 0
self.wt = {}
self.real_wt = {}
self.mkt = []
self.SetWarmUp(timedelta(350))
self.quandlCode = "RATEINF/INFLATION_USA"
Quandl.SetAuthCode("hcm-xaeGb6haorprzgnh")
self.cpi = self.AddData(QuandlCustomColumns, self.quandlCode, Resolution.Daily, TimeZones.NewYork).Symbol
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 100), #100), #1000), #1005 0), #100), #1000), #100100), #100), #1000), #1005 0), #100), #1000), #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):
current_inflation = self.Securities[self.cpi].Price
self.Debug('{}'.format(current_inflation))
vola = (self.history[[self.MKT]].pct_change().std() * np.sqrt(252))
wait_days = int(vola * DAY)
self.Debug('{}'.format(wait_days))
period = int((1.0 - vola) * BASE_RET)
r = self.history.pct_change(period).iloc[-1]
rGLD = round(((r[self.GLD] - r[self.SLV]) * 50), 100)
rXLU = round(((r[self.XLU] - r[self.XLI]) * 50), 100)
rUUP = round(((r[self.UUP] - r[self.DBB]) * 50), 100)
# self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.10))
exit1 = (r[self.XLI] < r[self.XLU]) and (r[self.SLV] < r[self.GLD])# and (r[self.DBB] < r[self.UUP])
exit2 = (r[self.XLI] < r[self.XLU]) and (r[self.SLV] < r[self.GLD]) and (r[self.DBB] < r[self.UUP])
if current_inflation > 5.8 :
exit = exit1
else:
exit = exit2
if exit:
self.bull = False
self.outday = self.count
if self.count >= self.outday + wait_days:
self.bull = True
self.count += 1
if current_inflation > 5.8 :
self.safe = self.BND2
else:
self.safe = self.BND1
if not self.bull:
for sec in self.ASSETS:
self.wt[sec] = LEV if sec is self.safe else 0 if sec is self.safe 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):
vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252)
period = int((1.0 - vola) * (BASE_RET))
r = self.history.pct_change(period).iloc[-1]
rGLD = round(((r[self.GLD] - r[self.SLV]) * 50), 100)
rXLU = round(((r[self.XLU] - r[self.XLI]) * 50), 100)
rUUP = round(((r[self.UUP] - r[self.DBB]) * 50), 100)
# rI = round(((r[self.RINF] - r[self.TLT]) * 50), 100)
self.Plot('ROC', 'GOLD/SLV', rGLD)
self.Plot('ROC', 'XLU/XLI', rXLU)
self.Plot('ROC', 'UUP/DBB', rUUP)
# self.Plot('ROC', 'RINF/TLT', rI)
vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252)
wait_days = int(vola * DAY)
self.Plot('Wait_days', 'Days', wait_days)
# mkt_price = self.Securities[self.BNCH].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))
# Crea una istanza della classe CPIData come simbolo personalizzato
class QuandlCustomColumns(PythonQuandl):
def __init__(self):
# Define ValueColumnName: cannot be None, Empty or non-existant column name
self.ValueColumnName = "Value"