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It should just require ".Symbol" - capital S as its case sensitive! The other coarse properties are here:

Thanks Jared. 

I implemented the computation of minimum volume (both in dollars and in shares) for n days as follows:

Volume_Dollar = x.DollarVolume
Volume_Share = x.Volume
self.min_dollar_volume = IndicatorExtensions.MIN(Volume_Dollar, self.value_MinVolumeDays)
self.min_volume = IndicatorExtensions.MIN(Volume_Share, self.value_MinVolumeDays)

However, I had the following errors:

Runtime Error: Trying to dynamically access a method that does not exist throws a TypeError exception. To prevent the exception, ensure each parameter type matches those required by the MIN method. Please checkout the API documentation.
at CoarseSelectionFunction in main.py:line 94
TypeError : No method matches given arguments for MIN (Open Stacktrace)

Is there another method of computing the minimum volume (both in dollars and in shares) for n days? 

Thanks a lot. 

PS: here's a part of my python program:

def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        #Risk Management Parameters
        TradingEquity = self.GetParameter("Starting Trading Equity") #Starting equity used for Trading
        FixedFractionalRisk = self.GetParameter("Fixed Fractional Risk") #Fraction of the equity risked in trading 
        
        #Screening Parameters
        MinVolumeDays = self.GetParameter("Days for Minimum Volume") #Number of days needed to compute the minimum number of volume (in shares and in dollar value) (default = 50 days)
        MinStockPrice = self.GetParameter("Minimum Stock Price") #Minimum price of stock 
        MaxPosition = self.GetParameter("Maximum Number of Positions") #Number of positions allowed 

        #Entry Parameters
        MADays = self.GetParameter("Moving Average Period") #Number of days needed to compute the simple moving average of stock price 
        ADXDays = self.GetParameter("ADX Period") #Number of days needed to compute the ADX 
        ADXThreshold = self.GetParameter("ADX Threshold") #Threshold used to determine if ADX produces buy signal. If ADX reaches above threshold, then the indicator produces a buy signal. (default = 45)
        ATRDays = self.GetParameter("ATR Period") #Number of days needed to compute the ATR
        RSIDays = self.GetParameter("RSI Period") #Number of days needed to compute the RSI
        RSIThreshold = self.GetParameter("RSI Threshold") #Threshold used to determine if RSI produces buy signal. If RSI reaches below threshold, then the indicator produces a buy signal. (default = 30)
        LimitOrderThreshold = self.GetParameter("Limit Order Threshold") #Threshold used to compute the limit price when the system signals to buy the stocks. The limit price = previous close - Limit Order Threshold*previous close  (default = 4%)

        #Exit Parameters
        ATRMultiplier = self.GetParameter("ATR Stop Loss Factor") #Multiplier of ATR used for computing the stop-loss
        ProfitTarget = self.GetParameter("Profit Target: ") #Threshold for Profit Target
        ExitDays = self.GetParameter("Exit Signal Period") #Number of days to wait for an exit signal (either a stop-loss or hitting the profit target). If there is neither a stop-loss or profit target, the system will exit after the exit signal period.

        #Set Default Parameter Values
        self.value_TradingEquity = 100,000 if TradingEquity is None else float(TradingEquity)
        self.value_FixedFractionalRisk = 0.02 if FixedFractionalRisk is None else float(FixedFractionalRisk)
        self.value_MinVolumeDays = 50 if MinVolumeDays is None else int(MinVolumeDays)
        self.value_MinStockPrice = 1 if MinStockPrice is None else float(MinStockPrice)
        self.value_MaxPosition = 10 if MaxPosition is None else int(MaxPosition) 
        self.value_MADays = 150 if MADays is None else int(MADays) 
        self.value_ADXDays = 7 if ADXDays is None else int(ADXDays)  
        self.value_ADXThreshold = 45 if ADXThreshold is None else int(ADXThreshold)
        self.value_ATRDays = 10 if ATRDays is None else int(ATRDays)
        self.value_RSIDays = 3 if RSIDays is None else int(RSIDays)
        self.value_RSIThreshold = 30 if RSIThreshold is None else int(RSIThreshold)
        self.value_LimitOrderThreshold = 0.04 if LimitOrderThreshold is None else float(LimitOrderThreshold)
        self.value_ATRMultiplier = 2.5 if ATRMultiplier is None else float(ATRMultiplier)
        self.value_ProfitTarget = 0.03 if ProfitTarget is None else float(ProfitTarget)
        self.value_ExitDays = 4 if ExitDays is None else int(ExitDays)
        
        self.SetStartDate(2017, 1, 1)                    #Set Start Date
        self.SetEndDate(2017, 6, 30)                     #Set End Date
        self.SetCash(100000)                             #Set Strategy Cash

        # use daily historical data and construct the universe according to the screennng criteria
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction)

        #To determine the number of positions and update existing positions, make a storage for the list of entry orders and exit orders
        self.__openLimitOrders = []          #Stores Entry Limit Orders not yet filled
        self.__filledLimitOrders = []        #Stores Entry Limit Orders that are filled (i.e., existing positions)
        self.__openStopMarketOrder = []      #Stores Exit Stop Market Orders not yet filled
        self.__filledStopMarketOrder = []    #Stores Exit Stop Market Orders that are filled
        
        #We also create a list of dates when there is no exit signal
        self.__daysWithoutExitSignal = []

    def CoarseSelectionFunction(self, coarse):
        
        Filtered_Universe = []
        
        #Runtime Error: AttributeError : 'CoarseFundamental' object has no attribute 'symbol'
            # at CoarseSelectionFunction in main.py:line 87
        #AttributeError : 'CoarseFundamental' object has no attribute 'symbol'     (Open Stacktrace)
        
        for x in coarse:
            ticker_symbol = x.Symbol
            Volume_Dollar = x.DollarVolume
            Volume_Share = x.Volume
            self.min_dollar_volume = IndicatorExtensions.MIN(Volume_Dollar, self.value_MinVolumeDays)
            self.min_volume = IndicatorExtensions.MIN(Volume_Share, self.value_MinVolumeDays)
            self.low_price = self.Securities[ticker_symbol].Low
            
            #We only want equities that satisfy the following screening criteria
                #US stocks, min volume of last 50 trading days above 500K shares
                #Min price of $1 per share 
                #Min dollar volume of $2.5M over same 50 day time period
            if self.min_dollar_volume > 2500000 and self.low_price > self.value_MinStockPrice and self.min_volume > 500000:
                Filtered_Universe.append(x)
      
        #For every stock that satisfy the screening criteria, compute the RSI
        for x in Filtered_Universe:
            x.RS_Index = RelativeStrengthIndex(x.Symbol, self.value_RSIDays, Resolution.Daily)
        
        #Then, sort the stocks according to the highest RSI:
        Filtered_Universe.sort(key=lambda x: x.RS_Index, reverse=True)      

        return [i.Symbol for i in Filtered_Universe]