| Overall Statistics |
|
Total Trades 8 Average Win 0% Average Loss 0% Compounding Annual Return -12.113% Drawdown 6.100% Expectancy 0 Net Profit -5.234% Sharpe Ratio -2.097 Probabilistic Sharpe Ratio 0.650% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.136 Beta 0.302 Annual Standard Deviation 0.048 Annual Variance 0.002 Information Ratio -2.536 Tracking Error 0.085 Treynor Ratio -0.334 Total Fees $0.00 |
class EMABasedStrategy(QCAlgorithm):
def Initialize(self): #Initialize Dates, Cash, Equities, Fees, Allocation, Parameters, Indicators, Charts
# Set Start Date, End Date, and Cash
#-------------------------------------------------------
self.SetTimeZone(TimeZones.NewYork) #EDIT: Added Timezon
self.SetStartDate(2011, 1, 1) # Set Start Date
self.SetEndDate(2011, 6, 1) # Set End Date
self.SetCash(10000) # Set Strategy Cash
#-------------------------------------------------------
# Set Custom Universe
#-------------------------------------------------------
self.AddUniverse(self.CoarseSelectionFilter, self.FineSelectionFilter)
self.UniverseSettings.Resolution = Resolution.Hour #Needs to change to Resolution.Minute once code works, leaving Daily for now to minimize data
self.UniverseSettings.SetDataNormalizationMode = DataNormalizationMode.SplitAdjusted
self.UniverseSettings.FeeModel = ConstantFeeModel(0.0)
self.UniverseSettings.Leverage = 1
self.SetBrokerageModel(BrokerageName.Alpaca, AccountType.Cash) #EDIT: Added Brokerage, appears to have set fees to zero
#-------------------------------------------------------
# Set Contants
#-------------------------------------------------------
self.EMA_Period_Fast = 20
self.EMA_Period_Slow = 200
self.EMA_Period_Medium = 50
self.__numberOfSymbols = 100
self.__numberOfSymbolsFine = 5
#-------------------------------------------------------
# Define Percentage Allocation and variables
#-------------------------------------------------------
self.percentagebuy = 0.05
self.stopLossPercent = 0.9
self.indicators = {}
self.highestSymbolPrice = -1
#-------------------------------------------------------
def CoarseSelectionFilter(self, coarse):
sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True) # sort descending by daily dollar volume
return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ] # return the symbol objects of the top entries from our sorted collection
def FineSelectionFilter(self, fine): # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
sortedByPeRatio = sorted(fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=False) # sort descending by P/E ratio
self.universe = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ] # take the top entries from our sorted collection
return self.universe
def OnSecuritiesChanged(self, changes):
# Create indicator for each new security
for security in changes.AddedSecurities:
self.indicators[security.Symbol] = SymbolData(security.Symbol, self, self.EMA_Period_Fast, self.EMA_Period_Slow, self.EMA_Period_Medium)
# for security in changes.RemovedSecurities:
# if security.Invested:
# self.Liquidate(security.Symbol, "Universe Removed Security")
# if security in self.indicators:
# self.indicators.pop(security.Symbol, None)
def OnData(self, data): #Entry Point for Data and algorithm - Check Data, Define Buy Quantity, Process Volume, Check Portfolio, Check RSI, Execute Buy/Sell orders, Chart Plots
for symbol in self.universe:
if not data.ContainsKey(symbol): #Tested and Valid/Necessary
continue
if data[symbol] is None: #Tested and Valid/Necessary
continue
if not symbol in self.indicators: #Tested and Valid/Necessary
continue
# Ensure indicators are ready to update rolling windows
if not self.indicators[symbol].slow_ema.IsReady:
continue
# Update EMA rolling windows
self.indicators[symbol].fast_ema_window.Add(self.indicators[symbol].get_fast_EMA())
self.indicators[symbol].slow_ema_window.Add(self.indicators[symbol].get_slow_EMA())
self.indicators[symbol].medium_ema_window.Add(self.indicators[symbol].get_medium_EMA())
# Check for Indicator Readiness within Rolling Window
#-------------------------------------------------------
if not (self.indicators[symbol].fast_ema_window.IsReady and self.indicators[symbol].slow_ema_window.IsReady and self.indicators[symbol].medium_ema_window.IsReady):
continue #return #EDIT
#EXECUTE TRADING LOGIC HERE -
# if self.Portfolio[symbol].Invested:
# # Sell condition
# if (self.indicators[symbol].fast_ema_window[1] >= self.indicators[symbol].slow_ema_window[1]) and (self.indicators[symbol].fast_ema_window[4] < self.indicators[symbol].slow_ema_window[4]):
# self.Liquidate(symbol)
# # Buy conditions
# elif self.Portfolio.MarginRemaining > 0.9 * self.percentagebuy * self.Portfolio.TotalPortfolioValue:
# if self.indicators[symbol].fast_ema_window[1] <= self.indicators[symbol].slow_ema_window[1] and \
# (self.indicators[symbol].fast_ema_window[4] > self.indicators[symbol].slow_ema_window[4]):
# self.buyquantity = round((self.percentagebuy*self.Portfolio.TotalPortfolioValue)/data[symbol].Close)
# self.MarketOrder(symbol, self.buyquantity)
if self.Portfolio[symbol].Invested:
# if self.indicators[symbol].is_buy_signal_liquidate():
# self.Debug(str(self.Time) + " Liquidating : " + str(symbol) + 'Quantity -->' + str(self.Portfolio[symbol].Quantity))
# self.Liquidate(symbol)
# el
if self.Securities[symbol].Close > self.highestSymbolPrice:
self.highestSymbolPrice = self.Securities[symbol].Close
updateFields = UpdateOrderFields()
updateFields.StopPrice = self.highestSymbolPrice * 0.9
self.stopMarketTicket.Update(updateFields)
self.Debug('inside trailing SL update OLD-->' + str(self.Securities[symbol].Close) + ' New -->' + str(updateFields.StopPrice))
# if self.Securities[symbol].Holdings.UnrealizedProfitPercent < 0.1:
# self.Debug('Liquidate after 20% loss on trade' + str(symbol))
# self.Liquidate(symbol)
if self.Portfolio.MarginRemaining > self.percentagebuy * self.Portfolio.TotalPortfolioValue:
if not self.Portfolio[symbol].Invested and self.indicators[symbol].is_buy_signal():
self.buyquantity = round((self.percentagebuy*self.Portfolio.TotalPortfolioValue)/data[symbol].Close)
ticket = self.MarketOrder(symbol, self.buyquantity)
# Put a trailing stop loss order at same time
self.stopMarketTicket = self.StopMarketOrder(symbol, self.buyquantity, self.stopLossPercent * data[symbol].Close)
updateSettings = UpdateOrderFields()
updateSettings.Tag = "Our New Tag for SPY Trade. Medium 0 -->" + str(self.indicators[symbol].medium_ema_window[0]) + 'Slow 0 ->' + str(self.indicators[symbol].slow_ema_window[0]) + 'Medium 1 ->' + str(self.indicators[symbol].medium_ema_window[1]) + 'Slow 1 ->' + str(self.indicators[symbol].slow_ema_window[1])
ticket.Update(updateSettings)
self.highestSymbolPrice = self.Securities[symbol].Close
class SymbolData(object):
rolling_window_length = 2
def __init__(self, symbol, context, fast_ema_period, slow_ema_period, medium_ema_period):
self.symbol = symbol
self.fast_ema_period = fast_ema_period
self.slow_ema_period = slow_ema_period
self.medium_ema_period = medium_ema_period
self.fast_ema = context.EMA(symbol, self.fast_ema_period, Resolution.Hour) #, fillDataForward = True, leverage = 1, extendedMarketHours = False)
self.slow_ema = context.EMA(symbol, self.slow_ema_period, Resolution.Hour) #, fillDataForward = True, leverage = 1, extendedMarketHours = False)
self.medium_ema = context.EMA(symbol, self.medium_ema_period, Resolution.Hour)
self.fast_ema_window = RollingWindow[float](self.rolling_window_length)
self.slow_ema_window = RollingWindow[float](self.rolling_window_length)
self.medium_ema_window = RollingWindow[float](self.rolling_window_length)
# Warm up EMA indicators
history = context.History([symbol], slow_ema_period + self.rolling_window_length, Resolution.Hour)
for time, row in history.loc[symbol].iterrows():
self.fast_ema.Update(time, row["close"])
self.slow_ema.Update(time, row["close"])
self.medium_ema.Update(time, row["close"])
# Warm up rolling windows
if self.fast_ema.IsReady:
self.fast_ema_window.Add(self.fast_ema.Current.Value)
if self.slow_ema.IsReady:
self.slow_ema_window.Add(self.slow_ema.Current.Value)
if self.medium_ema.IsReady:
self.medium_ema_window.Add(self.medium_ema.Current.Value)
def get_fast_EMA(self):
return self.fast_ema.Current.Value
def get_slow_EMA(self):
return self.slow_ema.Current.Value
def get_medium_EMA(self):
return self.medium_ema.Current.Value
def is_buy_signal(self):
if self.medium_ema_window[0] > self.slow_ema_window[0] and \
self.medium_ema_window[1] < self.slow_ema_window[1]:
if self.fast_ema_window[0] > self.slow_ema_window[0]:
return True
def is_buy_signal_liquidate(self):
return self.fast_ema_window[0] < self.slow_ema_window[0] and \
self.fast_ema_window[1] > self.slow_ema_window[1]