| Overall Statistics |
|
Total Trades 23 Average Win 4.44% Average Loss -4.98% Compounding Annual Return 220.812% Drawdown 22.100% Expectancy 0.547 Net Profit 57.695% Sharpe Ratio 3.233 Probabilistic Sharpe Ratio 77.247% Loss Rate 18% Win Rate 82% Profit-Loss Ratio 0.89 Alpha 1.035 Beta 2.709 Annual Standard Deviation 0.461 Annual Variance 0.213 Information Ratio 3.514 Tracking Error 0.377 Treynor Ratio 0.551 Total Fees $505.38 Estimated Strategy Capacity $7100000.00 Lowest Capacity Asset TQQQ UK280CGTCB51 Portfolio Turnover 11.61% |
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 IntelligentSkyRodent(QCAlgorithm):
def Initialize(self):
self.cash = 100000
self.buffer_pct = 0.085
self.SetStartDate(2023, 1, 1)
self.SetEndDate(2023, 5, 23)
self.SetCash(self.cash)
self.equities = ['VCIT', 'UDN','SARK','AMD','FNGU','TSLL','AEHR','MSTR','TARK','XLY','QQQE','VOOG','VOOV','VTV','HIBL','XLK','XLP','SVXY','QID','TBF','TSLA','LQD','VTIP','EDV','STIP','SPTL','IEI','USDU','SQQQ','VIXM','SPXU','QQQ','BSV','TQQQ','SPY','DBC','SHV','IAU','VEA','UTSL','UVXY','UPRO','EFA','EEM','TLT','SHY','GLD','SLV','USO','WEAT','CORN','SH','DRN','PDBC','COMT','KOLD','BOIL','ESPO','PEJ','UGL','URE','VXX','UUP','BND','BIL','DUST','JDST','JNUG','GUSH','DBA','DBB','COM','PALL','AGQ','BAL','WOOD','URA','SCO','UCO','DBO','TAGS','CANE','REMX','COPX','IEF','SPDN','CHAD','DRIP','SPUU','INDL','BRZU','ERX','ERY','CWEB','CHAU','KORU','MEXX','EDZ','EURL','YINN','YANG','TNA','TZA','SPXL','SPXS','MIDU','TYD','TYO','TMF','TMV','TECL','TECS','SOXL','SOXS','LABU','LABD','RETL','DPST','DRV','PILL','CURE','FAZ','FAS','EWA','EWGS','EWG','EWP','EWQ','EWU','EWJ','EWI','EWN','ECC','NURE','VNQI','VNQ','VDC','VIS','VGT','VAW','VPU','VOX','VFH','VHT','VDE','SMH','DIA','UDOW','PSQ','SOXX','VTI','COST','UNH','SPHB','BTAL','VIXY','WEBL','WEBS','UBT','PST','TLH','QLD','SQM','SSO','SD','DGRO','SCHD','SGOL','TIP','DUG','EWZ','TBX','VGIT','VGLT','CCOR','LBAY','NRGD','PHDG','SPHD','COWZ','CTA','DBMF','GDMA','VIGI','AGG','NOBL','FAAR','BITO','FTLS','MORT','FNDX','GLL','NTSX','RWL','VLUE','IJR','SPYG','VXUS','AAL','AEP','AFL','C','CMCSA','DUK','EXC','F','GM','GOOGL','INTC','JNJ','KO','MET','NWE','OXY','PFE','RTX','SNY','SO','T','TMUS','VZ','WFC','WMT','AMZN','MSFT','NVDA','TSM','BA','CB','COKE','FDX','GE','LMT','MRK','NVEC','ORCL','PEP','V','DBE','BRK-B','CRUS','INFY','KMLM','NSYS','SCHG','SGML','SLDP','ARKQ','XLU','XLV','ULTA','AAPL','AMZU','BAD','DDM','IYH','JPM','PM','XOM']
self.MKT = self.AddEquity("QQQ", Resolution.Daily).Symbol
self.mkt = []
for equity in self.equities:
self.AddEquity(equity, Resolution.Minute)
self.Securities[equity].SetDataNormalizationMode(DataNormalizationMode.Adjusted)
self.PT2 = 0.98
self.HT199 = 0
self.HT299 = 0
self.HT399 = 0
self.HT499 = 0
self.HT599 = 0
self.HT699 = 0
self.HT99 = 0
self.HTS199 = []
self.HTS299 = []
self.HTS399 = []
self.HTS499 = []
self.HTS599 = []
self.HTS699 = []
self.HTS99 = []
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 CalReturn(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 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.TQQQFTLT()
self.ExecuteTrade()
def TQQQFTLT(self):
if self.Securities['SPY'].Price > self.SMA('SPY', 200):
if self.RSI('TQQQ', 10) > 78:
self.HT99 = self.PT2
self.HTS99 = "UVXY"
else:
if self.RSI('SPXL', 10) > 79:
self.HT99 = self.PT2
self.HTS99 = "UVXY"
else:
if self.CalReturn('TQQQ', 4) > 0.2:
if self.RSI('TQQQ', 10) < 31:
self.HT99 = self.PT2
self.HTS99 = "TQQQ"
else:
if self.RSI('UVXY', 10) > self.RSI('SQQQ', 10):
self.HT99 = self.PT2
self.HTS99 = "UVXY"
else:
self.HT99 = self.PT2
self.HTS99 = "SQQQ"
else:
self.HT99 = self.PT2
self.HTS99 = "TQQQ"
else:
if self.RSI('TQQQ', 10) < 31:
self.HT99 = self.PT2
self.HTS99 = "TECL"
else:
if self.RSI('SMH', 10) < 30:
self.HT99 = self.PT2
self.HTS99 = "SOXL"
else:
if self.RSI('DIA', 10) < 27:
self.HT99 = self.PT2
self.HTS99 = "UDOW"
else:
if self.RSI('SPY', 14) < 28:
self.HT99 = self.PT2
self.HTS99 = "UPRO"
else:
self.Group1()
self.Group2()
def Group1(self):
if self.CalReturn('QQQ', 200) < -0.2:
if self.Securities['QQQ'].Price < self.SMA('QQQ', 20):
if self.CalReturn('QQQ', 60) < -0.12:
self.Group5()
self.Group6()
else:
if self.RSI('TLT', 10) > self.RSI('SQQQ', 10):
self.HT199 = 0.5*self.PT2
self.HTS199 = "TQQQ"
else:
self.HT199 = 0.5*self.PT2
self.HTS199 = "SQQQ"
else:
if self.RSI('SQQQ', 10) < 31:
self.HT199 = 0.5*self.PT2
self.HTS199 = "PSQ"
else:
if self.CalReturn('QQQ', 9) > 0.055:
self.HT199 = 0.5*self.PT2
self.HTS199 = "PSQ"
else:
if self.RSI('QQQ', 10) > self.RSI('SMH', 10):
self.HT199 = 0.5*self.PT2
self.HTS199 = "QQQ"
else:
self.HT199 = 0.5*self.PT2
self.HTS199 = "SMH"
else:
if self.Securities['QQQ'].Price < self.SMA('QQQ', 20):
if self.RSI('TLT', 10) > self.RSI('SQQQ', 10):
self.HT199 = 0.5*self.PT2
self.HTS199 = "TQQQ"
else:
self.HT199 = 0.5*self.PT2
self.HTS199 = "SQQQ"
else:
if self.RSI('SQQQ', 10) < 31:
self.HT199 = 0.5*self.PT2
self.HTS199 = "SQQQ"
else:
if self.CalReturn('QQQ', 9) > 0.055:
self.HT199 = 0.5*self.PT2
self.HTS199 = "SQQQ"
else:
if self.RSI('TQQQ', 10) > self.RSI('SOXL', 10):
self.HT199 = 0.5*self.PT2
self.HTS199 = "TQQQ"
else:
self.HT199 = 0.5*self.PT2
self.HTS199 = "SOXL"
def Group2(self):
if self.Securities['QQQ'].Price < self.SMA('QQQ', 20):
if self.CalReturn('QQQ', 60) < -0.12:
self.Group3()
self.Group4()
else:
if self.RSI('TLT', 10) > self.RSI('SQQQ', 10):
self.HT299 = 0.5*self.PT2
self.HTS299 = "TQQQ"
else:
self.HT299 = 0.5*self.PT2
self.HTS299 = "SQQQ"
else:
if self.RSI('SQQQ', 10) < 31:
for equity in self.equities:
if not equity == "SQQQ" and self.Portfolio[equity].HoldStock:
self.Liquidate(equity)
self.HT299 = 0.5*self.PT2
self.HTS299 = "SQQQ"
else:
if self.CalReturn('QQQ', 70) < -0.15:
if self.RSI('TQQQ', 10) > self.RSI('SOXL', 10):
self.HT299 = 0.5*self.PT2
self.HTS299 = "TQQQ"
else:
self.HT299 = 0.5*self.PT2
self.HTS299 = "SOXL"
else:
equities = ["SPY", "QQQ", "DIA", "XLP"]
returns = {}
for equity in equities:
returns[equity] = self.CalReturn(equity, 14)
s_e = sorted([item for item in returns.items() if item[1] is not None], key=lambda x: x[1], reverse=True)
top_2_equities = s_e[0]
self.HT299 = 0.5*self.PT2
self.HTS299 = top_2_equities[0]
def Group3(self):
if self.Securities['SPY'].Price > self.SMA('SPY', 20):
self.HT399 = 0.25*self.PT2
self.HTS399 = "SPY"
else:
if self.RSI('TLT', 10) > self.RSI('SQQQ', 10):
self.HT399 = 0.25*self.PT2
self.HTS399 = "QQQ"
else:
self.HT399 = 0.25*self.PT2
self.HTS399 = "PSQ"
def Group4(self):
if self.RSI('TLT', 10) > self.RSI('SQQQ', 10):
self.HT499 = 0.25*self.PT2
self.HTS499 = "QQQ"
else:
self.HT499 = 0.25*self.PT2
self.HTS499 = "PSQ"
def Group5(self):
if self.Securities['SPY'].Price > self.SMA('SPY', 20):
self.HT599 = 0.25*self.PT2
self.HTS599 = "SPY"
else:
if self.RSI('TLT', 10) > self.RSI('SQQQ', 10):
self.HT599 = 0.25*self.PT2
self.HTS599 = "QQQ"
else:
self.HT599 = 0.25*self.PT2
self.HTS599 = "PSQ"
def Group6(self):
if self.RSI('TLT', 10) > self.RSI('SQQQ', 10):
self.HT699 = 0.25*self.PT2
self.HTS699 = "QQQ"
else:
self.HT699 = 0.25*self.PT2
self.HTS699 = "PSQ"
def ExecuteTrade(self):
group99 = {
'HTS': [self.HTS99, self.HTS199, self.HTS299, self.HTS399, self.HTS499, self.HTS599, self.HTS699],
'HT': [self.HT99, self.HT199, self.HT299, self.HT399, self.HT499, self.HT599, self.HT699]
}
df99 = pd.DataFrame(group99)
df = pd.concat([df99])
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
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])
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
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])
else:
pass
self.HT199 = 0
self.HT299 = 0
self.HT399 = 0
self.HT499 = 0
self.HT599 = 0
self.HT699 = 0
self.HT99 = 0
self.HTS199 = []
self.HTS299 = []
self.HTS399 = []
self.HTS499 = []
self.HTS599 = []
self.HTS699 = []
self.HTS99 = []