def optimize( returns):
        mean_returns = returns.mean()
        matCov = returns.cov()
        coins = ["btc", "eth", "etc", "ltc", "dash", "neo", "zec", "xmr"]
        port = 50000
        results = np.zeros((4 + len(coins)-1, port))
        for i in range(port):
            weights = np.array(np.random.random(8))
            weights /= np.sum(weights)
            port_return = np.sum(mean_returns*weights)*1140
            port_stdv = np.sqrt(np.dot(weights.T,np.dot(matCov, weights)))*np.sqrt(1140)
            #Returns based on this portfolio
            results[0,i] = port_return
            #standard deviation based on this portfolio
            results[1,i] = port_stdv
            #Variance of this portfolio
            results[2,i] = results[0,i]/ (results[1,i])
            #3: BTC
            #4: ETH
            #5: ETC
            #6: LTC
            #7: DASH
            #8: NEO
            #9: ZEC
            #10: XMR
            for j in range(len(weights)):
                results[j+3,i]= weights[j]
        pd.DataFrame(results).to_csv("sharpeRatios50kResults.csv")
        plotResults(results)

    def plotResults(results):
        heat = results[0]/(results[2])
        scatter = []
        for i in range(50000):
            scatter.append([str(results[2][i]),
                               str(results[0][i]),
                               "BTC: "+str(results[3][i]),
                                "ETH: "+str(results[4][i]),
                                "ETC: "+str(results[5][i]),
                                "LTC: "+str(results[6][i]),
                                "DASH: "+str(results[7][i]),
                                "NEO: "+str(results[8][i]),
                                "ZEC: "+str(results[9][i]),
                                "XMR: "+str(results[10][i])])
            if results[0][i]/results[2][i] == max(heat):
                maxX = results[2][i]
                maxY = (results[0][i])
                recommendedPortfolio = [
                "Recommended Portfolio Distribution:",
                "BTC: "+str(results[3][i]),
                "ETH: "+str(results[4][i]),
                "ETC: "+str(results[5][i]),
                "LTC: "+str(results[6][i]),
                "DASH: "+str(results[7][i]),
                "NEO: "+str(results[8][i]),
                "ZEC: "+str(results[9][i]),
                "XMR: "+str(results[10][i])]
        plt.scatter(x = results[2], y = results[0], c=[str(point/255) for point in heat])
        plt.scatter(x = maxX, y = maxY, c = 200, s = 75, alpha = .2)
        plt.title('50,000 Portfolios, Sharpe Ratio')
        plt.xlabel('Volatility')
        plt.ylabel('Returns')
        plt.savefig("sharpeRatios50k.png")
        print()
        for line in recommendedPortfolio:
            print(line)
        pd.DataFrame(scatter).to_csv("scatterPoints.csv")
        pd.DataFrame(recommendedPortfolio).to_csv("recommendedPortfolio.csv")
        plt.show()