| Overall Statistics |
|
Total Trades 31 Average Win 0.11% Average Loss -0.10% Compounding Annual Return 1537.872% Drawdown 3.200% Expectancy -0.176 Net Profit 3.112% Sharpe Ratio 53.028 Probabilistic Sharpe Ratio 100.000% Loss Rate 60% Win Rate 40% Profit-Loss Ratio 1.06 Alpha 3.889 Beta 2.928 Annual Standard Deviation 0.113 Annual Variance 0.013 Information Ratio 66.81 Tracking Error 0.079 Treynor Ratio 2.045 Total Fees $45.72 Estimated Strategy Capacity $7800000.00 Lowest Capacity Asset ETHCY R735QTJ8XC9X |
class LiquidUniverseSelection(QCAlgorithm):
filteredByPrice = None
changes = None
def SellAll(self):
if self.FirstDay > 0:
self.Liquidate()
self.FirstDay = 0
self.FirstDay = self.FirstDay + 1
self.Debug(f"self.LastDay = {self.FirstDay} sstop")
self.Liquidate()
def __init__(self):
self.FirstDay = 0
self.SetWarmUp(10, Resolution.Daily)
self.SetSecurityInitializer(lambda x: x.SetMarketPrice(self.GetLastKnownPrice(x)))
def Initialize(self):
self.SetStartDate(2019, 1, 8)
self.SetEndDate(2019, 1, 12)
self.SetCash(100000)
self.AddUniverse(self.CoarseSelectionFilter)
self.SetAlpha(MOMAlphaModel())
self.SetPortfolioConstruction( UnEqualWeightingPortfolioConstructionModel() )
self.SetExecution(AndreExecutionModel())
def CoarseSelectionFilter(self, coarse):
for cf in coarse:
symbol = cf.Symbol
self.sortedByDollarVolume = sorted(coarse, key=lambda c: c.DollarVolume, reverse=True)
self.symbols_by_price = [c.Symbol for c in self.sortedByDollarVolume if c.Price > 10]
if len(self.symbols_by_price) < 100:
self.Top100 = self.symbols_by_price[:len(self.symbols_by_price)]
else:
self.Top100 = self.symbols_by_price[:100]
return self.Top100
class MOMAlphaModel(AlphaModel):
#self.lastDay = -1
def __init__(self):
self.mom = {}
self.lastDay = -1
def OnSecuritiesChanged(self, algorithm, changes):
for security in changes.AddedSecurities:
symbol = security.Symbol
self.last_return = 0
self.history = algorithm.History([symbol], 1, Resolution.Daily)
if self.history.empty:
return
algorithm.Debug(f"history.head(1)= {self.history.head(1)} stop_history")
if self.history.iloc[0]["close"]:
self.last_close = self.history.iloc[0]["close"]
else: self.last_close = 1
if self.history.iloc[0]["open"]:
self.last_open = self.history.iloc[0]["open"]
else: self.last_open = 1
self.last_return = (self.last_close - self.last_open) / self.last_open
self.mom[symbol] = self.last_return
algorithm.Debug(f"self.mom[{symbol}]= {self.mom[symbol]} self.mom_stop")
for security in changes.RemovedSecurities:
symbol = security.Symbol
algorithm.Liquidate(symbol)
self.mom.pop(symbol, None)
algorithm.Debug(f"popped= {symbol}")
pass
def Update(self, algorithm, data):
if algorithm.Time.day == self.lastDay:
return []
self.lastDay = algorithm.Time.day
symbols = self.mom.keys()
self.groep =[]
self.total_weight=0
for symbol in symbols:
self.last_return = 0
self.history = algorithm.History([symbol], 1, Resolution.Daily)
if self.history.empty:
continue
if self.history.iloc[0]["close"]:
self.last_close = self.history.iloc[0]["close"]
else: self.last_close = 1
if self.history.iloc[0]["open"]:
self.last_open = self.history.iloc[0]["open"]
else: self.Last_open = 1
self.last_return = (self.last_close - self.last_open) / self.last_open
self.mom[symbol] = self.last_return
direction = InsightDirection.Flat
magnitude = self.last_return
algorithm.Debug(f"magnitude= {magnitude} stop_magnitude")
if magnitude > 0.00001:
direction = InsightDirection.Up
algorithm.Debug(f"Up Up {direction} stop up")
if magnitude < -0.00001: direction = InsightDirection.Down
self.total_weight = self.total_weight + magnitude
visie=Insight.Price(symbol, timedelta(minutes = 2040), direction, None, None, None, magnitude)
self.groep.append(visie)
return Insight.Group(self.groep)
class AndreExecutionModel(ExecutionModel):
def __init__(self):
'''Initializes a new instance of the ImmediateExecutionModel class'''
self.targetsCollection = PortfolioTargetCollection()
self.lastDay = -1
def Execute(self, algorithm, targets):
'''Immediately submits orders for the specified portfolio targets.
Args:
algorithm: The algorithm instance
targets: The portfolio targets to be ordered'''
if algorithm.Time.day == self.lastDay:
return []
self.lastDay = algorithm.Time.day
self.targetsCollection.AddRange(targets)
if self.targetsCollection.Count > 0:
for target in self.targetsCollection.OrderByMarginImpact(algorithm):
quantity = OrderSizing.GetUnorderedQuantity(algorithm, target)
if quantity != 0:
#self.MarketOnOpenOrder("IBM", 100)
algorithm.MarketOnOpenOrder(target.Symbol, quantity)
algorithm.MarketOnCloseOrder(target.Symbol, -quantity)
self.targetsCollection.ClearFulfilled(algorithm)
pass
class UnEqualWeightingPortfolioConstructionModel(PortfolioConstructionModel):
def __init__(self):
self.targets = []
def CreateTargets(self, algorithm, insights):
total_weight = 0
for insight in insights:
total_weight = total_weight + insight.Weight
for insight in insights:
algorithm.Debug(f"insight= {insight} stop insight.")
algorithm.Debug(f"insight.Symbol= {insight.Symbol} stop insight.Symbol.")
algorithm.Debug(f"insight.Direction= {insight.Direction} stop insight.Direction.")
algorithm.Debug(f"insight.Weight= {insight.Weight} stop insight.Weight.")
#target = PortfolioTarget.Percent(algorithm, "IBM")
target_weight = insight.Weight / total_weight
algorithm.Debug(f"target_weight= {target_weight} stop target_weight.")
target = PortfolioTarget.Percent(algorithm, insight.Symbol, target_weight)
if target_weight > 0.05:
self.targets.append(target)
return self.targets