| Overall Statistics |
|
Total Trades 2 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees $5.06 Estimated Strategy Capacity $2700000.00 Lowest Capacity Asset TYH U8JOSZGR4OKL Portfolio Turnover 36.83% |
#region imports from AlgorithmImports import * #endregion # Your New Python File
from indicators import RSI, SMA, CumReturn, AH
from project.main import YellowCatStrat
class TQQQFTLTStrategy(YellowCatStrat):
def __init__(self, algorithm):
super().__init__()
self.algorithm = algorithm
def Execute(self):
if self.algorithm.Securities['SPY'].Price > SMA(self.algorithm, 'SPY', 200):
if RSI(self.algorithm, 'TQQQ', 10) > 78:
AH(self.algorithm, ['SPXU', 'UVXY', 'SQQQ'], 1, 0.33)
else:
if RSI(self.algorithm, 'SPXL', 10) > 79:
AH(self.algorithm, ['SPXU', 'UVXY', 'SQQQ'], 1, 0.33)
else:
if CumReturn(self.algorithm, 'TQQQ', 4) > 0.2:
if RSI(self.algorithm, 'TQQQ', 10) < 31:
AH(self.algorithm, 'TQQQ', 1, 1)
else:
if RSI(self.algorithm, 'UVXY', 10) > RSI(self.algorithm, 'SQQQ', 10):
AH(self.algorithm, ['SPXU', 'UVXY', 'SQQQ'], 1, 0.33)
else:
AH(self.algorithm, 'SQQQ', 1, 1)
else:
AH(self.algorithm, 'TQQQ', 1, 1)
else:
if RSI(self.algorithm, 'TQQQ', 10) < 31:
AH(self.algorithm, 'TECL', 1, 1)
else:
if RSI(self.algorithm, 'SMH', 10) < 30:
AH(self.algorithm, 'SOXL', 1, 1)
else:
if RSI(self.algorithm, 'DIA', 10) < 27:
AH(self.algorithm, 'UDOW', 1, 1)
else:
if RSI(self.algorithm, 'SPY', 14) < 28:
AH(self.algorithm, 'UPRO', 1, 1)
else:
self.Group1()
self.Group2()
def Group1(self):
if CumReturn(self.algorithm, 'QQQ', 200) < -0.2:
if self.algorithm.Securities['QQQ'].Price < SMA(self.algorithm, 'QQQ', 20):
if CumReturn(self.algorithm, 'QQQ', 60) < -0.12:
self.algorithm.Group5()
self.algorithm.Group6()
else:
if RSI(self.algorithm, 'TLT', 10) > RSI(self.algorithm, 'SQQQ', 10):
AH(self.algorithm, 'TQQQ', 1, 0.5)
else:
AH(self.algorithm, 'SQQQ', 1, 0.5)
else:
if RSI(self.algorithm, 'SQQQ', 10) < 31:
AH(self.algorithm, 'PSQ', 1, 0.5)
else:
if CumReturn(self.algorithm, 'QQQ', 9) > 0.055:
AH(self.algorithm, 'PSQ', 1, 0.5)
else:
if RSI(self.algorithm, 'TQQQ', 10) > RSI(self.algorithm, 'SOXL', 10):
AH(self.algorithm, 'TQQQ', 1, 0.5)
else:
AH(self.algorithm, 'SOXL', 1, 0.5)
def Group2(self):
if self.algorithm.Securities['QQQ'].Price < SMA(self.algorithm, 'QQQ', 20):
if CumReturn(self.algorithm, 'QQQ', 60) < -0.12:
self.algorithm.Group3()
self.algorithm.Group4()
else:
if RSI(self.algorithm, 'TLT', 10) > RSI(self.algorithm, 'SQQQ', 10):
AH(self.algorithm, 'TQQQ', 1, 0.5)
else:
AH(self.algorithm, 'SQQQ', 1, 0.5)
else:
if self.algorithm.RSI('TQQQ', 10) > self.algorithm.RSI('SOXL', 10):
AH(self.algorithm, 'TQQQ', 1, 0.5)
else:
AH(self.algorithm, 'SOXL', 1, 0.5)
def Group3(self):
if self.algorithm.Securities['SPY'].Price > SMA(self.algorithm, 'SPY', 20):
AH(self.algorithm, 'SPY', 1, 0.25)
else:
if RSI(self.algorithm, 'TLT', 10) > RSI(self.algorithm, 'SQQQ', 10):
AH(self.algorithm, 'QQQ', 1, 0.25)
else:
AH(self.algorithm, 'PSQ', 1, 0.25)
def Group4(self):
if RSI(self.algorithm, 'TLT', 10) > RSI(self.algorithm, 'SQQQ', 10):
AH(self.algorithm, 'QQQ', 1, 0.25)
else:
AH(self.algorithm, 'PSQ', 1, 0.25)
def Group5(self):
if self.algorithm.Securities['SPY'].Price > SMA(self.algorithm, 'SPY', 20):
AH(self.algorithm, 'SPY', 1, 0.25)
else:
if RSI(self.algorithm, 'TLT', 10) > RSI(self.algorithm, 'SQQQ', 10):
AH(self.algorithm, 'QQQ', 1, 0.25)
else:
AH(self.algorithm, 'PSQ', 1, 0.25)
def Group6(self):
if RSI(self.algorithm, 'TLT', 10) > RSI(self.algorithm, 'SQQQ', 10):
AH(self.algorithm, 'QQQ', 1, 0.25)
else:
AH(self.algorithm, 'PSQ', 1, 0.25)#region imports from AlgorithmImports import * #endregion # Your New Python File
from indicators import RSI, CumReturn, MaxDD, SMA, STD, IV, SMADayRet, EMA, Sort, AH
from project.main import YellowCatStrat
class TQQQorNotStrategy(YellowCatStrat):
def __init__(self, algorithm):
super().__init__()
self.algorithm = algorithm
def Execute(self):
if RSI(self.algorithm,'TQQQ',10) > 78:
AH(self.algorithm,['SPXU','UVXY','SQQQ'],3,1/3)
else:
if CumReturn(self.algorithm,'TQQQ',6) < -0.12:
if CumReturn(self.algorithm,'TQQQ',1) > 0.055:
AH(self.algorithm,['SPXU','UVXY','SQQQ'],3,1/3)
else:
if RSI(self.algorithm,'TQQQ',10) < 32:
AH(self.algorithm,'TQQQ',3,1)
else:
if MaxDD(self.algorithm,'TMF',10)<0.07:
AH(self.algorithm,'TQQQ',3,1)
else:
AH(self.algorithm,'BIL',3,1)
else:
if MaxDD(self.algorithm,'QQQ',10)>0.06:
AH(self.algorithm,'BIL',3,1)
else:
if MaxDD(self.algorithm,'TMF',10)>0.07:
AH(self.algorithm,'BIL',3,1)
else:
if self.algorithm.Securities['QQQ'].Price > self.algorithm.SMA('QQQ',25):
AH(self.algorithm,'TQQQ',3,1)
else:
if RSI(self.algorithm,'SPY',60) > 50:
if RSI(self.algorithm,'BND',45) > RSI(self.algorithm,'SPY',45):
AH(self.algorithm,'TQQQ',3,1)
else:
AH(self.algorithm,'BIL',3,1)
else:
if RSI(self.algorithm,'IEF',200) < RSI(self.algorithm,'TLT',200):
if RSI(self.algorithm,'BND',45) > RSI(self.algorithm,'SPY',45):
AH(self.algorithm,'TQQQ',3,1)
else:
AH(self.algorithm,'BIL',3,1)
else:
AH(self.algorithm,'BIL',3,1)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
import csv
import io
import time
import json
def RSI(algorithm,equity,period):
extension = min(period*5,250)
r_w = RollingWindow[float](extension)
history = algorithm.History(equity,extension - 1,Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < extension:
current_price = algorithm.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(algorithm,equity,period):
history = algorithm.History(equity,period,Resolution.Daily)
closing_prices = pd.Series([bar.Close for bar in history])
current_price = algorithm.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(algorithm,equity,period):
r_w = RollingWindow[float](period + 1)
r_w_return = RollingWindow[float](period)
history = algorithm.History(equity,period,Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < period + 1:
current_price = algorithm.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(algorithm,equity,period):
history = algorithm.History(equity,period - 1,Resolution.Daily)
closing_prices = pd.Series([bar.Close for bar in history])
current_price = algorithm.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(algorithm,equity,period):
r_w = RollingWindow[float](period)
history = algorithm.History(equity,period - 1,Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < period:
current_price = algorithm.Securities[equity].Price
r_w.Add(current_price)
if r_w.IsReady:
sma = sum(r_w)/period
return sma
else:
return 0
def IV(algorithm,equity,period):
r_w = RollingWindow[float](period + 1)
r_w_return = RollingWindow[float](period)
history = algorithm.History(equity,period,Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < period + 1:
current_price = algorithm.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(algorithm,equity,period):
r_w = RollingWindow[float](period + 1)
r_w_return = RollingWindow[float](period)
history = algorithm.History(equity,period,Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < period + 1:
current_price = algorithm.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(algorithm,equity,period):
extension = period + 50
r_w = RollingWindow[float](extension)
history = algorithm.History(equity,extension - 1,Resolution.Daily)
for historical_bar in history:
r_w.Add(historical_bar.Close)
while r_w.Count < extension:
current_price = algorithm.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(algorithm,sort_type,equities,period,reverse,number,multiplier):
algorithm.PT = getattr(algorithm,f"PT{number}") * multiplier
returns = {}
for equity in equities:
returns[equity] = getattr(algorithm,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]
ht = getattr(algorithm,f"HT{number}")
hts = getattr(algorithm,f"HTS{number}")
for i in ht.keys():
if ht[i] == 0:
ht[i] = algorithm.PT
hts[i].append(t3e[0][0])
break
setattr(algorithm,f"HT{number}",ht)
setattr(algorithm,f"HTS{number}",hts)
def AH(algorithm,equities,PTnumber,multiplier): #AppendHolding
if not isinstance(equities,list):
equities = [equities]
HT = getattr(algorithm,f"HT{PTnumber}")
HTS = getattr(algorithm,f"HTS{PTnumber}")
PT = getattr(algorithm,f"PT{PTnumber}") * multiplier
for equity in equities:
for i in HT.keys():
if HT[i] == 0:
HT[i] = PT
HTS[i].append(equity)
break# main.py
from AlgorithmImports import *
from indicators import *
class StrategyPerformanceTracker:
def __init__(self):
self.strategyReturns = {} # {strategyName: [daily returns]}
def logDailyReturn(self, strategyName, dailyReturn):
if strategyName not in self.strategyReturns:
self.strategyReturns[strategyName] = []
self.strategyReturns[strategyName].append(dailyReturn)
def calculateReturnsOverPeriod(self, strategyName, days):
if strategyName in self.strategyReturns:
return sum(self.strategyReturns[strategyName][-days:])
return None
def rankStrategies(self, days, topN):
performance = {strategy: self.calculateReturnsOverPeriod(strategy, days)
for strategy in self.strategyReturns}
rankedStrategies = sorted(performance.items(), key=lambda x: x[1], reverse=True)
return rankedStrategies[:topN]
class YellowCatStrat(QCAlgorithm):
def Initialize(self):
self.cash = 100000
self.buffer_pct = 0.02
self.SetStartDate(2023,10,27)
self.SetEndDate(2023,10,27)
self.SetCash(self.cash)
self.equities = ['XENE','ARCT','CRSP','IMRX','NAMS','BPMC','IMUX','HOWL','AUTL','ETNB','SIMO','IEO','ATXS','SERA','VRTX','PNT','FUSN','PYXS','EXAI','ICVX','IOVA','CRBU','ROIV','XLF','CDTX','TRDA','CRVS','AKBA','EDC','SON','XLE','TWM','RWM','URTY','PBR','OIL','ROST','WMS','AAPD','TSLQ','TSLS','AAPB','ALGN','TPL','COIN','VLO','AA','BITI','HIBS','ACLS','EQT','MOS','AR','MU','CZR','UVIX','ENPH','AMEH','ERIC','GNRC','BULZ','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','EUO','YCS','MVV','USD','TMF','SPXL','EPI','IYK','CURE','DIG','XLU']
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.AddEquity('BIL',Resolution.Minute)
self.Securities['BIL'].SetDataNormalizationMode(DataNormalizationMode.TotalReturn)
from Strategies.TQQQFTLT.version1 import TQQQFTLTStrategy
self.tqqqftltStrategy = TQQQFTLTStrategy(self)
from Strategies.TQQQorNot.version1 import TQQQorNotStrategy
self.tqqqorNotStrategy = TQQQorNotStrategy(self)
self.PTMaster = 1
self.PT1 = 0.24*self.PTMaster #TQQQFTLT
self.PT3 = 0.13*self.PTMaster #TQQQorNOT
self.HT1 = {str(i).zfill(2): 0 for i in range(1,10)}
self.HTS1 = {str(i).zfill(2): [] for i in range(1,10)}
self.HT3 = {str(i).zfill(2): 0 for i in range(1,10)}
self.HTS3 = {str(i).zfill(2): [] for i in range(1,10)}
self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.BeforeMarketClose("SPY",2),
self.FunctionBeforeMarketClose)
def __init__(self):
super().__init__()
def FunctionBeforeMarketClose(self):
# End of day trading function
self.tqqqftltStrategy.Execute()
self.tqqqorNotStrategy.Execute()
self.ExecuteTrade()
def OnData(self, data):
# This function is called every time new data is received
pass
def ExecuteTrade(self):
group1 = {
'HTS': [self.HTS1[i][0] if len(self.HTS1[i]) == 1 else self.HTS1[i] for i in self.HTS1],
'HT': [self.HT1[i] for i in self.HT1]
}
df1 = pd.DataFrame(group1)
group3 = {
'HTS': [self.HTS3[i][0] if len(self.HTS3[i]) == 1 else self.HTS3[i] for i in self.HTS3],
'HT': [self.HT3[i] for i in self.HT3]
}
df3 = pd.DataFrame(group3)
df = pd.concat([df1,df3])
df['HTS'] = df['HTS'].astype(str)
result = df.groupby(['HTS']).sum().reset_index()
# Dictionary with pairs
pairs_dict = {'SOXL':'SOXS','TQQQ':'SQQQ','SPXL':'SPXS','WEBL':'WEBS','TECL':'TECS','UPRO':'SPXU','QQQ':'PSQ','SPY':'SH','TMV':'TMF','HIBL':'HIBS','BITO':'BITI','TSLA':'TSLS','AAPL':'AAPD','ERX':'ERY','BOIL':'KOLD'}
pairs_dict.update({v: k for k,v in pairs_dict.items()}) #ensure both directions are covered
# Track selling and buying
processed_pairs_selling = set()
processed_pairs_buying = set()
liquidated_equities = set()
# Exclude symbols
exclude_symbols = ['BIL','BSV','SHV','SHY']
# dictionary
symbol_dict = dict(zip(result.iloc[:,0],result.iloc[:,1]))
# Log output
output = "*****"
for symbol, percentage in symbol_dict.items():
output += "{}: {}% - ".format(symbol, round(percentage*100, 2))
output = output.rstrip(" - ")
self.Log(output)
# Symbols to be transformed
transform_symbols = ['PSQ','SH','USDU','SPXU','UPRO','QLD','QID','TSLS']
transform_mapping = {'PSQ':'SQQQ','SH':'SPXS','USDU':'UUP','SPXU':'SPXS','UPRO':'SPXL','QLD':'TQQQ','QID':'SQQQ','TSLS':'TSLQ'}
transform_ratios = {'PSQ':3,'SH':3,'USDU':1,'SPXU':1,'UPRO':1,'QLD':1.5,'QID':1.5,'TSLS':1}
# Transform symbols
for symbol in transform_symbols:
if symbol in symbol_dict:
new_symbol = transform_mapping[symbol]
ratio = transform_ratios[symbol]
new_percentage = symbol_dict[symbol]/ratio
# Adjust percentage allocation
if new_symbol in symbol_dict:
new_percentage += symbol_dict[new_symbol]
symbol_dict[new_symbol] = new_percentage
# Remove transformed
symbol_dict.pop(symbol, None)
# Ensure updated equities list
updated_equities = set(symbol_dict.keys())
# Liquidate equities
for equity in self.equities:
if equity not in updated_equities and self.Portfolio[equity].HoldStock and equity not in liquidated_equities:
self.Liquidate(equity)
liquidated_equities.add(equity)
# Iterate pairs selling
for symbol1,symbol2 in pairs_dict.items():
if symbol1 in symbol_dict and symbol2 in symbol_dict:
offset_value = abs(symbol_dict[symbol1] - symbol_dict[symbol2])
if symbol_dict[symbol1] >= symbol_dict[symbol2] and self.Portfolio[symbol2].HoldStock:
self.Liquidate(symbol2)
elif symbol_dict[symbol1] <= symbol_dict[symbol2] and self.Portfolio[symbol1].HoldStock:
self.Liquidate(symbol1)
# Mark processed selling
processed_pairs_selling.add(symbol1)
processed_pairs_selling.add(symbol2)
# Iterate remaining selling
for symbol,value in symbol_dict.items():
if symbol not in processed_pairs_selling and not value == 0 and symbol not in exclude_symbols:
percentage_equity = self.Portfolio[symbol].HoldingsValue/self.Portfolio.TotalPortfolioValue
if value < percentage_equity and abs(value/percentage_equity - 1) > self.buffer_pct:
self.SetHoldings(symbol,value)
# Iterate pairs buying
for symbol1,symbol2 in pairs_dict.items():
if symbol1 in symbol_dict and symbol2 in symbol_dict and symbol1 not in processed_pairs_buying and symbol2 not in processed_pairs_buying:
offset_value = abs(symbol_dict[symbol1] - symbol_dict[symbol2])
if offset_value > 0.01:
if symbol_dict[symbol1] > symbol_dict[symbol2]:
self.SetHoldings(symbol1,offset_value)
else:
self.SetHoldings(symbol2,offset_value)
else:
if self.Portfolio[symbol1].HoldStock:
self.Liquidate(symbol1)
if self.Portfolio[symbol2].HoldStock:
self.Liquidate(symbol2)
# Mark as processed buying
processed_pairs_buying.add(symbol1)
processed_pairs_buying.add(symbol2)
# Filter less than 1%
updated_equities = {symbol for symbol, value in symbol_dict.items() if value >= 0.01}
# Iterate remaining symbol_dict for buying
for symbol,value in symbol_dict.items():
if (symbol in updated_equities and
symbol not in processed_pairs_buying and
symbol not in exclude_symbols):
percentage_equity = (self.Portfolio[symbol].HoldingsValue /
self.Portfolio.TotalPortfolioValue)
if value > percentage_equity and abs(percentage_equity/value - 1) > self.buffer_pct:
self.SetHoldings(symbol,value)
self.HT1 = {str(i).zfill(2): 0 for i in range(1,10)}
self.HTS1 = {str(i).zfill(2): [] for i in range(1,10)}
self.HT3 = {str(i).zfill(2): 0 for i in range(1,10)}
self.HT3 = {str(i).zfill(2): 0 for i in range(1,10)}
self.HTS3 = {str(i).zfill(2): [] for i in range(1,10)}