| Overall Statistics |
|
Total Trades 98 Average Win 1.31% Average Loss -0.68% Compounding Annual Return 21.661% Drawdown 10.400% Expectancy 0.894 Net Profit 38.086% Sharpe Ratio 1.383 Probabilistic Sharpe Ratio 66.664% Loss Rate 35% Win Rate 65% Profit-Loss Ratio 1.93 Alpha 0.034 Beta 0.472 Annual Standard Deviation 0.11 Annual Variance 0.012 Information Ratio -0.856 Tracking Error 0.114 Treynor Ratio 0.322 Total Fees $2771.30 Estimated Strategy Capacity $690000.00 Lowest Capacity Asset NQ XUERCWA6EWAP |
# region imports
from AlgorithmImports import *
# endregion
class CryingYellowGreenCrocodile(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 1, 1) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
self.nq = self.AddFuture("NQ", Resolution.Daily) # is the same as:
self.nq.SetFilter(0, 240)
# self.AddFuture(Futures.Indices.NASDAQ100EMini)
# self.min = self.MIN(self.nq, 14, Resolution.Minute)
# self.max = self.MAX(self.nq, 14, Resolution.Minute)
self.symbols = []
self.count = 0
self.day_count = 0
def OnData(self, data):
chains_count = 0
for chain in data.FutureChains:
self.day_count += 1
chains_count += 1
self.count += 1
contracts = [contract for contract in chain.Value if contract.Expiry > self.Time + timedelta(days=8)]
sorted_contracts = sorted(contracts, key=lambda x: x.Expiry)
if len(sorted_contracts) == 0:
continue
# for x in contracts:
self.symbols.append(str(sorted_contracts[0].Symbol))
if self.count < 20:
self.Debug(f'{chains_count}, @{self.Time}')
def OnEndOfAlgorithm(self):
self.Debug(f'Day amount; {self.day_count}')
self.Debug(f'len(symbols) = {len(self.symbols)}')
self.Debug(f'{self.symbols}')
# conclusion: yes, it selects the correct one# region imports
from AlgorithmImports import *
# endregion
class CryingYellowGreenCrocodile(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 5, 10)
self.SetEndDate(2022, 1, 1)
self.SetCash(10000000)
self.nq = self.AddFuture(Futures.Indices.NASDAQ100EMini, Resolution.Minute)
self.nq.SetFilter(8, 240)
self.Indicators = {
'sma': SimpleMovingAverage(11),
'ema_fast': ExponentialMovingAverage(100),
'ema_slow': ExponentialMovingAverage(300),
'min': Minimum(14),
'max': Maximum(14),
}
self.tolerance = 0.001
self.symbol = []
self.consolidator = None
def OnSecuritiesChanged(self, changes):
for security in changes.RemovedSecurities:
if len(changes.RemovedSecurities) > 0:
# Remove the consolidator for the previous contract
# and reset the indicators
if self.symbol is not None and self.consolidator is not None:
if self.symbol == security.Symbol:
self.SubscriptionManager.RemoveConsolidator(self.symbol, self.consolidator)
for indicator in self.Indicators:
self.Indicators[indicator].Reset()
# We don't need to call Liquidate(_symbol),
# since its positions are liquidated because the contract has expired.
for security in changes.AddedSecurities:
if len(changes.AddedSecurities) == 0: return
# Only one security will be added: the new front contract
self.symbol = changes.AddedSecurities[0].Symbol
# Create a new consolidator and register the indicators to it
self.consolidator = self.ResolveConsolidator(self.symbol, Resolution.Minute)
for indicator in self.Indicators:
self.RegisterIndicator(self.symbol, self.Indicators[indicator], self.consolidator)
self.WarmUpIndicator(self.symbol, self.Indicators[indicator], Resolution.Minute)
def OnData(self, data):
if not (self.Time.hour == 10 and self.Time.minute == 1): return
holding = None if self.symbol is None else self.Portfolio.get(self.symbol)
# if holding is None: return
if not data.Bars.ContainsKey(self.symbol): return
if not holding.Invested:
if self.Indicators['ema_fast'].Current.Value > (1 + self.tolerance)*self.Indicators['ema_slow'].Current.Value:
self.SetHoldings(self.symbol, 0.025)
elif holding.Invested:
if self.Indicators['ema_fast'].Current.Value < (1 - self.tolerance)*self.Indicators['ema_slow'].Current.Value:
self.Liquidate(self.symbol)