| Overall Statistics |
|
Total Trades 690 Average Win 1.70% Average Loss -1.22% Compounding Annual Return 310.289% Drawdown 24.000% Expectancy 0.502 Net Profit 620.603% Sharpe Ratio 4.758 Probabilistic Sharpe Ratio 99.670% Loss Rate 37% Win Rate 63% Profit-Loss Ratio 1.39 Alpha 1.859 Beta 0.073 Annual Standard Deviation 0.39 Annual Variance 0.152 Information Ratio 4.444 Tracking Error 0.425 Treynor Ratio 25.271 Total Fees $16138.85 Estimated Strategy Capacity $730000.00 Lowest Capacity Asset TMV UBTUG7D0B7TX Portfolio Turnover 46.38% |
from AlgorithmImports import *
import math
import pandas as pd
from cmath import sqrt
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data.Custom import *
from QuantConnect.Python import PythonData
class IntelligentSkyDoge(QCAlgorithm):
def Initialize(self):
self.cash = 100000
self.buffer_pct = 0.03 # CHANGE YOUR THRESHOLD HERE
self.SetStartDate(2022, 1, 1)
self.SetEndDate(2023, 5, 28)
self.SetCash(self.cash)
self.equities = ['SPY', 'QQQ', 'UVXY', 'SQQQ', 'SPXS', 'TECL', 'STIP', 'TQQQ', 'SHY', 'UPRO', 'QLD', 'UDN', 'TMV', 'SOXL', 'SHV', 'TLT', 'UUP', 'UGL', 'BIL', 'VCIT', 'VIXY', 'IEF', 'SVXY', 'EDV', 'QQQE', 'VTV', 'VOX', 'VOOG', 'VOOV', 'XLP', 'XLY', 'FAS', 'BIL', 'VCIT', 'VIXM', 'TMF']
self.MKT = self.AddEquity("SPY", Resolution.Daily).Symbol
self.mkt = []
for equity in self.equities:
self.AddEquity(equity, Resolution.Minute)
self.Securities[equity].SetDataNormalizationMode(DataNormalizationMode.Adjusted)
self.PT1 = 0.72
self.PT2 = 0.0
self.PT3 = 0.22
self.HT40 = {str(i).zfill(2): 0 for i in range(1, 21)}
self.HTS40 = {str(i).zfill(2): [] for i in range(1, 21)}
self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.BeforeMarketClose("SPY", 2),
self.FunctionBeforeMarketClose)
def RSI(self, equity, period):
extension = min(period*5, 250)
r_w = RollingWindow[float](extension)
history = self.History(equity, extension - 1, Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < extension:
current_price = self.Securities[equity].Price
r_w.Add(current_price)
if r_w.IsReady:
average_gain = 0
average_loss = 0
gain = 0
loss = 0
for i in range(extension - 1, extension - period -1, -1):
gain += max(r_w[i-1] - r_w[i], 0)
loss += abs(min(r_w[i-1] - r_w[i], 0))
average_gain = gain/period
average_loss = loss/period
for i in range(extension - period - 1, 0, -1):
average_gain = (average_gain*(period-1) + max(r_w[i-1] - r_w[i], 0))/period
average_loss = (average_loss*(period-1) + abs(min(r_w[i-1] - r_w[i], 0)))/period
if average_loss == 0:
return 100
else:
rsi = 100 - (100/(1 + average_gain / average_loss))
return rsi
else:
return None
def CumReturn(self, equity, period):
history = self.History(equity, period, Resolution.Daily)
closing_prices = pd.Series([bar.Close for bar in history])
current_price = self.Securities[equity].Price
closing_prices = closing_prices.append(pd.Series([current_price]))
first_price = closing_prices.iloc[0]
if first_price == 0:
return None
else:
return_val = (current_price / first_price) - 1
return return_val
def STD(self, equity, period):
r_w = RollingWindow[float](period + 1)
r_w_return = RollingWindow[float](period)
history = self.History(equity, period, Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < period + 1:
current_price = self.Securities[equity].Price
r_w.Add(current_price)
for i in range (period, 0, -1):
daily_return = (r_w[i-1]/r_w[i] - 1)
r_w_return.Add(daily_return)
dfstd = pd.DataFrame({'r_w_return':r_w_return})
if r_w.IsReady:
std = dfstd['r_w_return'].std()
if std == 0:
return 0
else:
return std
else:
return 0
def MaxDD(self, equity, period):
history = self.History(equity, period - 1, Resolution.Daily)
closing_prices = pd.Series([bar.Close for bar in history])
current_price = self.Securities[equity].Price
closing_prices = closing_prices.append(pd.Series([current_price]))
rolling_max = closing_prices.cummax()
drawdowns = (rolling_max - closing_prices) / rolling_max
max_dd = drawdowns.min()
return max_dd
def SMA(self, equity, period):
r_w = RollingWindow[float](period)
history = self.History(equity, period - 1, Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < period:
current_price = self.Securities[equity].Price
r_w.Add(current_price)
if r_w.IsReady:
sma = sum(r_w) / period
return sma
else:
return 0
def IV(self, equity, period):
r_w = RollingWindow[float](period + 1)
r_w_return = RollingWindow[float](period)
history = self.History(equity, period, Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < period + 1:
current_price = self.Securities[equity].Price
r_w.Add(current_price)
for i in range (period, 0, -1):
if r_w[i] == 0:
return 0
else:
daily_return = (r_w[i-1]/r_w[i] - 1)
r_w_return.Add(daily_return)
dfinverse = pd.DataFrame({'r_w_return':r_w_return})
if r_w.IsReady:
std = dfinverse['r_w_return'].std()
if std == 0:
return 0
else:
inv_vol = 1 / std
return inv_vol
else:
return 0
def SMADayRet(self, equity, period):
r_w = RollingWindow[float](period + 1)
r_w_return = RollingWindow[float](period)
history = self.History(equity, period, Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < period + 1:
current_price = self.Securities[equity].Price
r_w.Add(current_price)
for i in range (period, 0, -1):
if r_w[i] == 0:
return None
daily_return = (r_w[i-1]/r_w[i] - 1)
r_w_return.Add(daily_return)
if r_w.IsReady:
smareturn = sum(r_w_return) / period
return smareturn
else:
return 0
def EMA(self, equity, period):
extension = period + 50
r_w = RollingWindow[float](extension)
history = self.History(equity, extension - 1, Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < extension:
current_price = self.Securities[equity].Price
r_w.Add(current_price)
if r_w.IsReady:
total_price = 0
for i in range(extension - 1, extension - period - 2, -1):
total_price += r_w[i]
average_price = total_price/period
for i in range(extension - period - 2, -1, -1):
average_price = r_w[i]*2/(period+1) + average_price*(1-2/(period+1))
return average_price
else:
return None
def Sort(self, sort_type, equities, period, reverse, number, multiplier):
self.PT = getattr(self, f"PT{number}") * multiplier
returns = {}
for equity in equities:
returns[equity] = getattr(self, sort_type)(equity, period)
s_e = sorted([item for item in returns.items() if item[1] is not None], key = lambda x: x[1], reverse = reverse)
t3e = s_e[:1]
for i in self.HT40.keys():
if self.HT40[i] == 0:
self.HT40[i] = self.PT
self.HTS40[i].append(t3e[0][0])
break
def AppendHolding(self, equity, PTnumber, HTnumber, multiplier):
HT = getattr(self, f"HT{HTnumber}")
HTS = getattr(self, f"HTS{HTnumber}")
PT = getattr(self, f"PT{PTnumber}") * multiplier
for i in HT.keys():
if HT[i] == 0:
HT[i] = PT
HTS[i].append(equity)
break
def OnData (self, data):
pass
def FunctionBeforeMarketClose(self):
mkt_price = self.History(self.MKT, 2, Resolution.Daily)['close'].unstack(level= 0).iloc[-1]
self.mkt.append(mkt_price)
mkt_perf = self.cash * self.mkt[-1] / self.mkt[0]
self.Plot('Strategy Equity', self.MKT, mkt_perf)
self.HolyGrailSimplified()
#self.V15A()
self.v41Pops()
self.ExecuteTrade()
def HolyGrailSimplified(self):
if self.Securities['SPY'].Price > self.SMA('SPY', 200):
if self.RSI('QQQ', 10) > 79:
self.Sort("RSI", ["UVXY", "SQQQ"], 13, True, 1, 1)
elif self.RSI('SPY', 10) > 79:
self.Sort("RSI", ["UVXY", "SPXS"], 13, True, 1, 1)
else:
self.Sort("RSI", ["TECL", "STIP"], 11, False, 1, 1)
elif self.RSI('TQQQ', 10) < 31:
self.Sort("RSI", ["TECL", "SHY"], 11, False, 1, 1)
elif self.RSI('UPRO', 10) < 31:
self.Sort("RSI", ["UPRO", "SHY"], 11, False, 1, 1)
elif self.CumReturn('TQQQ', 6) < -0.11:
self.BuythedipsV2()
else:
if self.Securities['QLD'].Price > self.SMA('QLD', 20):
self.Sort("RSI", ["TECL", "STIP"], 11, False, 1, 1)
else:
self.Substrategy5()
self.Substrategy6()
def BuythedipsV2(self):
if self.CumReturn('TQQQ', 1) > 0.055:
self.Sort("RSI", ["UVXY", "SQQQ"], 11, True, 1, 1)
elif self.CumReturn('SQQQ', 1) > 0.028:
self.Substrategy1()
self.Substrategy2()
else:
self.HoldStocksBondsSOXLSHVTECLSTIP()
def Substrategy1(self):
self.Sort("RSI", ["TECL", "UDN"], 11, False, 1, 0.5)
def Substrategy2(self):
self.Sort("RSI", ["TECL", "TMV"], 11, True, 1, 0.5)
def HoldStocksBondsSOXLSHVTECLSTIP(self):
self.Substrategy3()
self.Substrategy4()
def Substrategy3(self):
self.Sort("RSI", ["SOXL", "SHV"], 11, False, 1, 0.68)
def Substrategy4(self):
self.Sort("RSI", ["TECL", "STIP"], 11, False, 1, 0.32)
def Substrategy5(self):
if self.SMADayRet('TLT', 20) > self.SMADayRet('UDN', 20):
self.Sort("RSI", ["TLT", "SQQQ"], 11, True, 1, 0.5)
else:
self.Sort("RSI", ["UUP", "SQQQ"], 10, False, 1, 0.5)
def Substrategy6(self):
self.Sort("RSI", ["UGL", "SQQQ"], 12, False, 1, 0.5)
def V15A(self):
if self.RSI('SPY', 6) > 75:
self.Sort("RSI", ["UVXY", "VIXY"], 5, False, 2, 1)
else:
if self.RSI('BIL', 5) < self.RSI('VCIT', 4):
self.AppendHolding('SOXL', 2, 40, 1)
else:
if self.SMADayRet('VIXY', 5) > self.SMADayRet('BND', 5):
self.Sort("MaxDD", ["UVXY", "VIXY", "IEF"], 3, True, 2, 1)
else:
if self.SMADayRet('EDV', 11) > 0:
self.Sort("RSI", ["SVXY", "TMF"], 21, False, 2, 1)
else:
self.Sort("RSI", ["SVXY", "TMV"], 21, False, 2, 1)
def v41Pops(self):
if self.RSI('QQQE', 10) > 79:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.RSI('VTV', 10) > 79:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.RSI('VOX', 10) > 79:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.MaxDD('SPY', 9) < 0:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.RSI('TECL', 10) > 79:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.RSI('VOOG', 10) > 79:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.RSI('VOOV', 10) > 79:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.RSI('XLP', 10) > 75:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.RSI('TQQQ', 10) > 79:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.RSI('XLY', 10) > 80:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.RSI('FAS', 10) > 80:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.RSI('SPY', 10) > 80:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
elif self.CumReturn('TQQQ', 6) < -0.12:
if self.CumReturn('QQQ', 1) > 0.018:
self.Sort("RSI", ["UVXY", "VIXY"], 13, False, 3, 1)
else:
self.V15B()
else:
self.V15B()
def V15B(self):
if self.RSI('SPY', 6) > 75:
self.Sort("RSI", ["UVXY", "VIXY"], 13, True, 3, 1)
else:
if self.RSI('BIL', 5) < self.RSI('VCIT', 5):
self.AppendHolding('SOXL', 3, 40, 1)
else:
if self.RSI('SVXY', 11) > 60:
self.Sort("RSI", ["TMF", "BIL"], 10, True, 3, 1)
else:
if self.SMADayRet('VIXM', 10) > self.SMADayRet('SVXY', 5):
self.Sort("MaxDD", ["UVXY", "VIXY", "IEF"], 3, False, 3, 1)
else:
if self.SMADayRet('EDV', 11) > 0:
self.Sort("RSI", ["SVXY", "TMF"], 21, True, 3, 1)
else:
self.Sort("RSI", ["SVXY", "TMV"], 21, True, 3, 1)
def ExecuteTrade(self):
group = {
'HTS': [self.HTS40[i][0] if len(self.HTS40[i]) == 1 else self.HTS40[i] for i in self.HTS40],
'HT': [self.HT40[i] for i in self.HT40]
}
df = pd.DataFrame(group)
df = pd.concat([df])
df['HTS'] = df['HTS'].astype(str)
result = df.groupby(['HTS']).sum().reset_index()
for equity in self.equities:
if all(not pd.isnull(result.iloc[i, 0]) and not equity == result.iloc[i, 0] for i in range(len(result))):
if self.Portfolio[equity].HoldStock:
self.Liquidate(equity)
output = "*****"
for i in range(len(result)):
if result.iloc[i, 0]:
percentage = round(result.iloc[i, 1] * 100, 2)
output += "{}: {}% - ".format(result.iloc[i, 0], percentage)
output = output.rstrip(" - ")
self.Log(output)
for i in range(len(result)):
if not result.iloc[i, 1] == 0 and not result.iloc[i, 0] == 'BIL':
percentage_equity = self.Portfolio[result.iloc[i, 0]].HoldingsValue / self.Portfolio.TotalPortfolioValue
quantity = (result.iloc[i, 1] - percentage_equity) * self.Portfolio.TotalPortfolioValue / self.Securities[result.iloc[i, 0]].Price
if result.iloc[i, 1] < percentage_equity and abs(result.iloc[i, 1] / percentage_equity - 1) > self.buffer_pct:
self.SetHoldings(result.iloc[i, 0], result.iloc[i, 1])
#self.MarketOnCloseOrder(result.iloc[i, 0], quantity)
else:
pass
for i in range(len(result)):
if not result.iloc[i, 1] == 0 and not result.iloc[i, 0] == 'BIL':
percentage_equity = self.Portfolio[result.iloc[i, 0]].HoldingsValue / self.Portfolio.TotalPortfolioValue
quantity = (result.iloc[i, 1] - percentage_equity) * self.Portfolio.TotalPortfolioValue / self.Securities[result.iloc[i, 0]].Price
if result.iloc[i, 1] > percentage_equity and abs(percentage_equity / result.iloc[i, 1] - 1) > self.buffer_pct:
self.SetHoldings(result.iloc[i, 0], result.iloc[i, 1])
#self.MarketOnCloseOrder(result.iloc[i, 0], quantity)
else:
pass
self.HT40 = {str(i).zfill(2): 0 for i in range(1, 21)}
self.HTS40 = {str(i).zfill(2): [] for i in range(1, 21)}