Backtest

for_loop.py

for signal in signals:
    if signal.lower() == 'moving average':
        print ('-'*60)
        print ('Simple Moving Average: ')
        # Let's calulate Simple Moving Average(SMA)
        short_sma= 20
        long_sma = 50
        SMAs=[short_sma, long_sma]
        
        for i in SMAs:
            df["SMA_"+str(i)]= df.iloc[:,4].rolling(window=i).mean()
        
        position=0 # 1 means we have already entered poistion, 0 means not already entered
        counter=0
        percentChange=[]   # empty list to collect %changes 
        for i in df.index:
            SMA_short=df['SMA_20']
            SMA_long =df['SMA_50']
            close=df['Adj Close'][i]
            
            if(SMA_short[i] > SMA_long[i]):

                if(position==0):
                    buyP=close   #buy price
                    position=1   # turn position

                
            elif(SMA_short[i] < SMA_long[i]):

                if(position==1):   # have a poistion in down trend
                    position=0     # selling position
                    sellP=close    # sell price
    
                    perc=(sellP/buyP-1)*100
                    percentChange.append(perc)
            if(counter==df["Adj Close"].count()-1 and position==1):
                position=0
                sellP=close

                perc=(sellP/buyP-1)*100
                percentChange.append(perc)
        
            counter+=1
        
        gains=0
        numGains=0
        losses=0
        numLosses=0
        totReturn=1
        for i in percentChange:
            if(i>0):
                gains+=i
                numGains+=1
            else:
                losses+=i
                numLosses+=1
            totReturn = totReturn*((i/100)+1)
        totReturn=round((totReturn-1)*100,2)
        print("These statistics are from "+str(start)+" up till now with "+str(numGains+numLosses)+" trades:")
        print("SMAs used: "+str(SMAs))
        print("Total return over "+str(numGains+numLosses)+ " trades: "+ str(totReturn)+"%")
        
        if (numGains>0):
            avgGain=gains/numGains
            maxReturn= str(max(percentChange))
        else:
            avgGain=0
            maxReturn=np.nan
        
        if(numLosses>0):
            avgLoss=losses/numLosses
            maxLoss=str(min(percentChange))
            ratioRR=str(-avgGain/avgLoss)  # risk-reward ratio
        else:
            avgLoss=0
            maxLoss=np.nan
            ratioRR='inf'
        
        df['PC'] = df['Close'].pct_change()
        hold = round(df['PC'].sum() * 100, 2)
        print ("Total return for a B&H strategy: " + str(hold)+'%')
        print("Average Gain: "+ str(round(avgGain, 2)))
        print("Average Loss: "+ str(round(avgLoss, 2)))
        print("Max Return: "+ str(maxReturn))
        print("Max Loss: "+ str(maxLoss))
        print("Gain/loss ratio: "+ str(ratioRR))
        
        if(numGains>0 or numLosses>0):
            batAvg=numGains/(numGains+numLosses)
        else:
            batAvg=0
        print("Success Rate: "+ str(batAvg))
        change.append(totReturn)
        trades = numGains+numLosses
        num_of_trades.append(trades)
        last_sell.append(sellP)
        last_buy.append(buyP)
        average_gain.append(avgGain)
        average_loss.append(avgLoss)
        max_return.append(float(maxReturn))
        max_loss.append(float(maxLoss))
        gain_loss.append(float(ratioRR))
        success_rate.append(batAvg)