| Overall Statistics |
|
Total Trades 1412 Average Win 2.34% Average Loss -2.67% Compounding Annual Return -40.017% Drawdown 93.600% Expectancy -0.007 Net Profit -63.359% Sharpe Ratio 0.278 Probabilistic Sharpe Ratio 9.747% Loss Rate 47% Win Rate 53% Profit-Loss Ratio 0.88 Alpha 0.114 Beta 1.201 Annual Standard Deviation 1.155 Annual Variance 1.335 Information Ratio 0.132 Tracking Error 1.128 Treynor Ratio 0.267 Total Fees $112228.26 Estimated Strategy Capacity $2000000.00 Lowest Capacity Asset ONON XRV6S79HAOO5 |
class PensiveRedOrangeCaribou(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 1, 1) # Set Start Date
self.SetCash(1000000) # Set Strategy Cash
self.AddUniverse(self.CoarseSelectionFilter)
self.UniverseSettings.Resolution = Resolution.Minute
self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw
self.symbol_data_by_symbol={}
def CoarseSelectionFilter(self, coarse):
sortedByDollarVolume = sorted(coarse, key=lambda c: c.DollarVolume, reverse=True)
return [ x.Symbol for x in sortedByDollarVolume if x.Price > 10 and x.HasFundamentalData][:500]
def OnSecuritiesChanged(self, changes):
for security in changes.RemovedSecurities:
symbol = security.Symbol
if security.Invested:
self.Liquidate(symbol)
symbol_data = self.symbol_data_by_symbol.pop(symbol, None)
if symbol_data:
symbol_data.dispose()
for security in changes.AddedSecurities:
symbol = security.Symbol
self.symbol_data_by_symbol[symbol] = SymbolData(self, symbol)
def OnData(self, data):
bestGain = None
bestGainValue = 0
numPositions = 0
for symbol, symbol_data in self.symbol_data_by_symbol.items():
if not (data.ContainsKey(symbol) and data[symbol] is not None and symbol_data.openingBar is not None and symbol_data.fhmin is not None):
return
if (self.Time.hour==12 and self.Time.minute ==0):
if (self.Portfolio[symbol].Invested):
numPositions = numPositions +1
if (bestGainValue == 0):
bestGainValue = symbol_data.percentChange
bestGain = symbol
else:
if (bestGainValue < symbol_data.percentChange):
bestGainValue = symbol_data.percentChange
bestGain = symbol
for symbol, symbol_data in self.symbol_data_by_symbol.items():
if not (data.ContainsKey(symbol) and data[symbol] is not None and symbol_data.openingBar is not None and symbol_data.fhmin is not None):
return
# if current price greater than open at 1% - open long position
if not self.Portfolio[symbol].Invested:
if (self.Time.hour==12 and self.Time.minute ==0):
if (data[symbol].Close > symbol_data.openingBar.Open*(1+3/100) and not symbol_data.wastrade and symbol == bestGain):
self.SetHoldings(symbol, 1/(numPositions+1))
symbol_data.wastrade = True
else:
# modify
if (self.Time.hour==12 and self.Time.minute ==0):
self.SetHoldings(symbol, 1/(numPositions+1))
# close if current price less than 1HMin.
if ((self.Time.hour==10 and self.Time.minute >=30) or
(self.Time.hour>10)):
if (data[symbol].Low < symbol_data.fhmin):
self.Liquidate(symbol)
class SymbolData:
openingBar = None
def __init__(self, algorithm, symbol):
self.algorithm = algorithm
self.symbol = symbol
self.fhmin = None
self.wastrade = False
self.percentChange = None
# Setup minute consolidator
self.five_minute_consolidator = TradeBarConsolidator(timedelta(minutes=5))
self.five_minute_consolidator.DataConsolidated += self.five_minute_consolidation_handler
algorithm.SubscriptionManager.AddConsolidator(symbol, self.five_minute_consolidator)
def five_minute_consolidation_handler(self, sender, bar):
if bar.Time.hour == 9 and bar.Time.minute == 30:
self.openingBar = bar
self.fhmin = bar.Low
self.wastrade = False
else:
if (bar.Time.hour == 9 and bar.Time.minute >30) or (bar.Time.hour==10 and bar.Time.minute <=30):
if (self.fhmin!= None and bar.Low < self.fhmin):
self.fhmin = bar.Low
if (bar.Time.hour == 11 and bar.Time.minute ==30):
if (self.openingBar!=None):
self.percentChange = bar.Close/self.openingBar.Open-1
def dispose(self):
self.algorithm.SubscriptionManager.RemoveConsolidator(self.symbol, self.five_minute_consolidator)