Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
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
$0.00
from datetime import timedelta
import fnmatch

class FollowingtheTrend(QCAlgorithm):

    def Initialize(self):

        self.SetStartDate(2017,1,1)  #Set Start Date
        self.SetEndDate(2017,1,5)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash

        self.symbols = [
            Futures.Indices.SP500EMini,
            Futures.Metals.Gold,
            #"CL",
            #"CG",
            #"HG",
            #"XK",
        ]
        
        self.fast = {}
        self.slow = {}
        for symbol in self.symbols:
            future = self.AddFuture(symbol, Resolution.Daily)
            future.SetFilter(timedelta(0), timedelta(182))
            
            # Note use of future.Symbol, if we use symbol string we get error regarding unsubbed asset(so use the object instead)
            self.fast[symbol] = SimpleMovingAverage(10)
            self.slow[symbol] = SimpleMovingAverage(100)
            self.ATR[symbol] = AverageTrueRange(20)

        self.chart_target = 'Strategy Equitys'
        sPlot = Chart(self.chart_target)
        
        # Set Risk Factor for position sizing
        self.risk_factor = 0.002
        
        '''
        # Lets chart the info
        for i,symbol in enumerate(self.symbols):
            sPlot.AddSeries(Series('%s'%symbol, SeriesType.Line, 2+i)) # Label only
            sPlot.AddSeries(Series('Fast %s'%symbol, SeriesType.Line, 2+i))
            sPlot.AddSeries(Series('Slow %s'%symbol, SeriesType.Line, 2+i))
            sPlot.AddSeries(Series('Current %s'%symbol, SeriesType.Line, 2+i))
        self.AddChart(sPlot)
        '''
        
    def OnData(self, data):
        # Used examples from here to access futures contracts
        # https://github.com/QuantConnect/Lean/blob/fb7d1994ff0b1859e7204ffedd609236a6fd4827/Algorithm.Python/BasicTemplateFuturesAlgorithm.py
            
        for chain in data.FutureChains:
             # Get contracts expiring no earlier than in 90 days
            contracts = filter(lambda x: x.Expiry > self.Time + timedelta(90), chain.Value)

            # if there is any contract, trade the front contract
            if len(contracts) == 0: continue
            front = sorted(contracts, key = lambda x: x.Expiry, reverse=True)[0]
            
            # get the corresponding underlying? has to be a better way to keep track...
            for base_symbol in self.symbols:
                if len(fnmatch.filter([str(front.Symbol)], str(base_symbol)+'*')) > 0:
                    # match found, plot or log
                    lookup_symbol = base_symbol
                    
                    self.fast[lookup_symbol].Update(self.Time, front.LastPrice)
                    self.slow[lookup_symbol].Update(self.Time, front.LastPrice)
                    
                    if self.fast[lookup_symbol].Current.Value > self.slow[lookup_symbol].Current.Value:
                        # Calculate number of shares to buy
                        #quantity = self.target_portfolio[symbol].ATR.Current.Value/(Portfolio.TotalPortfolioValue*self.risk_factor)
                        quantity = (Portfolio.TotalPortfolioValue*self.risk_factor)/self.target_portfolio[lookup_symbol].ATR.Current.Value
                        
                        # Send Orders
                        self.MarketOrder(lookup_symbol, quantity)
                        
                    elif self.fast[lookup_symbol] < self.slow[lookup_symbol]:
                        self.Liquidate(lookup_symbol)
                    else:
                        continue
                    
                    #self.slow[lookup_symbol]
                    #self.Log(str(lookup_symbol) + " : " + str(self.fast[lookup_symbol].Current.Value))
                    #self.Log(str(lookup_symbol) + " : " + str(self.slow[lookup_symbol].Current.Value))
                    #self.Plot(self.chart_target,'Fast %s'%lookup_symbol, self.fast[lookup_symbol].Current.Value)
                    #self.Plot(self.chart_target,'Slow %s'%lookup_symbol, self.slow[lookup_symbol].Current.Value)
                    #self.Plot(self.chart_target,'Current %s'%lookup_symbol, front.LastPrice)
                    #break
        #self.previous = self.Time
        
        
class SymbolData(object):
    def __init__(self, symbol, context):
        self.symbol = symbol
        """
        I had to pass ATR from outside object to get it to work, could pass context and use any indica
        var atr = ATR(Symbol symbol, int period, MovingAverageType type = null, Resolution resolution = null, Func`2[Data.IBaseData,Data.Market.IBaseDataBar] selector = null)
        """
        self.ATR = context.ATR(symbol, 20)

    """
    Runtime Error: Python.Runtime.PythonException: NotSupportedException : AverageTrueRange does not support Update(DateTime, decimal) method overload. Use Update(IBaseDataBar) instead.
    """
        
        # Use the below update method for ATR which is used for position sizing
    def update_value(self, time, value):
        self.ATR.Update(time, value)                        
from datetime import timedelta
import fnmatch

class IndicatorTest(QCAlgorithm):

    def Initialize(self):

        self.SetStartDate(2012,1,1)  #Set Start Date
        self.SetEndDate(2017,1,5)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash

        self.symbols = [
            Futures.Indices.SP500EMini,
            Futures.Metals.Gold,
            #"CL",
            #"CG",
            #"HG",
            #"XK",
        ]
        
        # Create Dictionaries for Indicators
        self.fast = {}
        self.slow = {}
        self.atr = {}
        
        # Add Data & Indicators
        for symbol in self.symbols:
            future = self.AddFuture(symbol, Resolution.Daily)
            future.SetFilter(timedelta(0), timedelta(182))
            
            # Note use of future.Symbol, if we use symbol string we get error regarding unsubbed asset(so use the object instead)
            self.fast[symbol] = SimpleMovingAverage(10)
            self.slow[symbol] = SimpleMovingAverage(100)
            self.atr[symbol] = AverageTrueRange(20)
            
        '''
        self.chart_target = 'Strategy Equity'
        sPlot = Chart(self.chart_target)
        
        
        # Lets chart the info
        for i,symbol in enumerate(self.symbols):
            sPlot.AddSeries(Series('%s'%symbol, SeriesType.Line, 2+i)) # Label only
            sPlot.AddSeries(Series('Fast %s'%symbol, SeriesType.Line, 2+i))
            sPlot.AddSeries(Series('Slow %s'%symbol, SeriesType.Line, 2+i))
            sPlot.AddSeries(Series('Current %s'%symbol, SeriesType.Line, 2+i))
        self.AddChart(sPlot)
        '''
        
    def OnData(self, data):
        # Used examples from here to access futures contracts
        # https://github.com/QuantConnect/Lean/blob/fb7d1994ff0b1859e7204ffedd609236a6fd4827/Algorithm.Python/BasicTemplateFuturesAlgorithm.py
            
        for chain in data.FutureChains:
             # Get contracts expiring no earlier than in 90 days
            contracts = filter(lambda x: x.Expiry > self.Time + timedelta(90), chain.Value)

            # if there is any contract, trade the front contract
            if len(contracts) == 0: continue
            front = sorted(contracts, key = lambda x: x.Expiry, reverse=True)[0]
            
            # get the corresponding underlying? has to be a better way to keep track...
            for base_symbol in self.symbols:
                if len(fnmatch.filter([str(front.Symbol)], str(base_symbol)+'*')) > 0:
                    # match found, plot or log
                    lookup_symbol = base_symbol
                    
                    self.fast[lookup_symbol].Update(self.Time, front.LastPrice)
                    self.slow[lookup_symbol].Update(self.Time, front.LastPrice)
                    
                    if self.fast[lookup_symbol].Current.Value > self.slow[lookup_symbol].Current.Value:
                        self.SetHoldings(lookup_symbol, 0.5)
                    elif self.fast[lookup_symbol].Current.Value < self.slow[lookup_symbol].Current.Value:
                        self.SetHoldings(lookup_symbol, 0)
                    
                    #self.Log(str(lookup_symbol) + " : " + str(self.fast[lookup_symbol].Current.Value))
                    #self.Log(str(lookup_symbol) + " : " + str(self.slow[lookup_symbol].Current.Value))
                    #self.Plot(self.chart_target,'Fast %s'%lookup_symbol, self.fast[lookup_symbol].Current.Value)
                    #self.Plot(self.chart_target,'Slow %s'%lookup_symbol, self.slow[lookup_symbol].Current.Value)
                    #self.Plot(self.chart_target,'Current %s'%lookup_symbol, front.LastPrice)
                    #break
        #self.previous = self.Time