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      "body": "@@ -23700,16 +23700,355 @@\n ed both.\n+ Running a simple plot with the DGDBTC pair on 15m we get the following:%0A!%5Bdgdbtcrsi.png%5D(https://steemitimages.com/DQmTUDbAn6aSCG1QBmeHy96nTWjQwvbWYfWxadiMXcNwBSy/dgdbtcrsi.png)%0AFigure 1: Sample RSI Plot%0A%0AThis plot looks accurate compared to TradingView, but is a little messy and may not give the best trading strategy right off the bat.\n  Due to \n",
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      "parent_author": "",
      "parent_permlink": "cryptocurrency",
      "permlink": "part-3-adding-backtesting-and-support-for-rsi-indicator",
      "title": "Cyptocurrency Trading Bot Part 3 - Adding Backtesting and Support for RSI Indicator"
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      "author": "genesiscrypto",
      "body": "# Step 1 – Code Clean Up\nAfter the previous part, we have some functional code to give us a look at the MACD indicator for a particular trading pair, but the code is not particularly useful for a number of reasons. If we wanted to add additional indicators or strategies, the duplicate code would stack up very quickly, not practical. We’ll instead create a new Strategy class which will handle all of the indicator calculation, strategy calculation (finding buy and sell points), and plotting. With this change our complete code now becomes:\n```python3.6\nfrom binance.client import Client\nimport talib as ta\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom datetime import datetime\n\nclass Trader:\n    def __init__(self, file):\n        self.connect(file)\n\n    \"\"\" Creates Binance client \"\"\"\n    def connect(self,file):\n        lines = [line.rstrip('\\n') for line in open(file)]\n        key = lines[0]\n        secret = lines[1]\n        self.client = Client(key, secret)\n\n    \"\"\" Gets all account balances \"\"\"\n    def getBalances(self):\n        prices = self.client.get_withdraw_history()\n        return prices\n\nclass Strategy:\n\n    def __init__(self, indicator_name, strategy_name, pair, interval, klines):\n        self.indicator = indicator_name\n        self.strategy = strategy_name\n        self.pair = pair\n        self.interval = interval\n        self.klines = klines\n        self.indicator_result = self.calculateIndicator()\n        self.strategy_result = self.calculateStrategy()\n\n\n    def calculateIndicator(self):\n        if self.indicator == 'MACD':\n            close = [float(entry[4]) for entry in self.klines]\n            close_array = np.asarray(close)\n\n            macd, macdsignal, macdhist = ta.MACD(close_array, fastperiod=12, slowperiod=26, signalperiod=9)\n            return [macd, macdsignal, macdhist]\n\n        else:\n            return None\n\n\n    def calculateStrategy(self):\n        if self.indicator == 'MACD':\n            if self.strategy == 'CROSS':\n                open_time = [int(entry[0]) for entry in self.klines]\n                new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n                crosses = []\n                macdabove = False\n                for i in range(len(self.indicator_result[0])):\n                    if np.isnan(self.indicator_result[0][i]) or np.isnan(self.indicator_result[1][i]):\n                        pass\n                    else:\n                        if self.indicator_result[0][i] > self.indicator_result[1][i]:\n                            if macdabove == False:\n                                macdabove = True\n                                cross = [new_time[i],self.indicator_result[0][i] , 'go']\n                                crosses.append(cross)\n                        else:\n                            if macdabove == True:\n                                macdabove = False\n                                cross = [new_time[i], self.indicator_result[0][i], 'ro']\n                                crosses.append(cross)\n                return crosses\n\n            else:\n                return None\n        else:\n            return None\n\n\n\n    def plotIndicator(self):\n        if self.indicator == 'MACD':\n            open_time = [int(entry[0]) for entry in klines]\n            new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n            plt.style.use('dark_background')\n            plt.plot(new_time, self.indicator_result[0], label='MACD')\n            plt.plot(new_time, self.indicator_result[1], label='MACD Signal')\n            plt.plot(new_time, self.indicator_result[2], label='MACD Histogram')\n            for entry in self.strategy_result:\n                plt.plot(entry[0], entry[1], entry[2])\n            title = \"MACD Plot for \" + self.pair + \" on \" + self.interval\n            plt.title(title)\n            plt.xlabel(\"Open Time\")\n            plt.ylabel(\"Value\")\n            plt.legend()\n            plt.show()\n\n        else:\n            pass\n\n\n\n\nfilename = 'credentials.txt'\ntrader = Trader(filename)\ntrading_pair = 'BTCUSDT'\ninterval = '1d'\nklines = trader.client.get_klines(symbol=trading_pair,interval=interval)\nmacd_strategy = Strategy('MACD','CROSS',trading_pair,interval,klines)\nmacd_strategy.plotIndicator()\n\n\n```\n\nThis is much more efficient, and will for us to expand the functionality of our script with ease in the future.\n\n# Step 2 – Backtesting\nMoving forward, backtesting will be a crucial part of this project, and it starts right here. We’re going to create a simple backtesting class with init parameters for starting amount and a strategy object, and an output with ending amount, percent change, number of trades, percentage of profitable trades, and a detailed report of each trade executed. After adding this functionality (and some more comments for readability), our code looks like this:\n```python3.6\nfrom binance.client import Client\nimport talib as ta\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom datetime import datetime\n\nclass Trader:\n    def __init__(self, file):\n        self.connect(file)\n\n    \"\"\" Creates Binance client \"\"\"\n    def connect(self,file):\n        lines = [line.rstrip('\\n') for line in open(file)]\n        key = lines[0]\n        secret = lines[1]\n        self.client = Client(key, secret)\n\n    \"\"\" Gets all account balances \"\"\"\n    def getBalances(self):\n        prices = self.client.get_withdraw_history()\n        return prices\n\nclass Strategy:\n\n    def __init__(self, indicator_name, strategy_name, pair, interval, klines):\n        #Name of indicator\n        self.indicator = indicator_name\n        #Name of strategy being used\n        self.strategy = strategy_name\n        #Trading pair\n        self.pair = pair\n        #Trading interval\n        self.interval = interval\n        #Kline data for the pair on given interval\n        self.klines = klines\n        #Calculates the indicator\n        self.indicator_result = self.calculateIndicator()\n        #Uses the indicator to run strategy\n        self.strategy_result = self.calculateStrategy()\n\n\n    '''\n    Calculates the desired indicator given the init parameters\n    '''\n    def calculateIndicator(self):\n        if self.indicator == 'MACD':\n            close = [float(entry[4]) for entry in self.klines]\n            close_array = np.asarray(close)\n\n            macd, macdsignal, macdhist = ta.MACD(close_array, fastperiod=12, slowperiod=26, signalperiod=9)\n            return [macd, macdsignal, macdhist]\n\n        else:\n            return None\n\n\n    '''\n    Runs the desired strategy given the indicator results\n    '''\n    def calculateStrategy(self):\n        if self.indicator == 'MACD':\n\n            if self.strategy == 'CROSS':\n                open_time = [int(entry[0]) for entry in self.klines]\n                new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n                self.time = new_time\n                crosses = []\n                macdabove = False\n                #Runs through each timestamp in order\n                for i in range(len(self.indicator_result[0])):\n                    if np.isnan(self.indicator_result[0][i]) or np.isnan(self.indicator_result[1][i]):\n                        pass\n                    #If both the MACD and signal are well defined, we compare the 2 and decide if a cross has occured\n                    else:\n                        if self.indicator_result[0][i] > self.indicator_result[1][i]:\n                            if macdabove == False:\n                                macdabove = True\n                                #Appends the timestamp, MACD value at the timestamp, color of dot, buy signal, and the buy price\n                                cross = [new_time[i],self.indicator_result[0][i] , 'go', 'BUY', self.klines[i][4]]\n                                crosses.append(cross)\n                        else:\n                            if macdabove == True:\n                                macdabove = False\n                                #Appends the timestamp, MACD value at the timestamp, color of dot, sell signal, and the sell price\n                                cross = [new_time[i], self.indicator_result[0][i], 'ro', 'SELL', self.klines[i][4]]\n                                crosses.append(cross)\n                return crosses\n\n            else:\n                return None\n        else:\n            return None\n\n    '''\n    Getter for the strategy result\n    '''\n    def getStrategyResult(self):\n        return self.strategy_result\n\n    '''\n    Getter for the klines\n    '''\n    def getKlines(self):\n        return self.klines\n\n    '''\n    Getter for the trading pair\n    '''\n    def getPair(self):\n        return self.pair\n\n    '''\n    Getter for the trading interval\n    '''\n    def getInterval(self):\n        return self.interval\n\n    '''\n    Getter for the time list\n    '''\n    def getTime(self):\n        return self.time\n\n    '''\n    Plots the desired indicator with strategy buy and sell points\n    '''\n    def plotIndicator(self):\n        if self.indicator == 'MACD':\n            open_time = [int(entry[0]) for entry in klines]\n            new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n            plt.style.use('dark_background')\n            plt.plot(new_time, self.indicator_result[0], label='MACD')\n            plt.plot(new_time, self.indicator_result[1], label='MACD Signal')\n            plt.plot(new_time, self.indicator_result[2], label='MACD Histogram')\n            for entry in self.strategy_result:\n                plt.plot(entry[0], entry[1], entry[2])\n            title = \"MACD Plot for \" + self.pair + \" on \" + self.interval\n            plt.title(title)\n            plt.xlabel(\"Open Time\")\n            plt.ylabel(\"Value\")\n            plt.legend()\n            plt.show()\n\n        else:\n            pass\n\nclass Backtest:\n    def __init__(self, starting_amount, start_datetime, end_datetime, strategy):\n        #Starting amount\n        self.start = starting_amount\n        #Number of trades\n        self.num_trades = 0\n        #Number of profitable trades\n        self.profitable_trades = 0\n        #Running amount\n        self.amount = self.start\n        #Start of desired interval\n        self.startTime = start_datetime\n        #End of desired interval\n        self.endTime = end_datetime\n        #Strategy object\n        self.strategy = strategy\n        #Trading pair\n        self.pair = self.strategy.getPair()\n        #Trading interval\n        self.interval = self.strategy.getInterval()\n        #Outputs the trades exectued\n        self.trades = []\n        #Runs the backtest\n        self.results = self.runBacktest()\n        #Prints the results\n        self.printResults()\n\n\n    def runBacktest(self):\n        amount = self.start\n        klines = self.strategy.getKlines()\n        time = self.strategy.getTime()\n        point_finder = 0\n        strategy_result = self.strategy.getStrategyResult()\n        #Finds the first cross point within the desired backtest interval\n        while strategy_result[point_finder][0] < self.startTime:\n            point_finder += 1\n        #Initialize to not buy\n        active_buy = False\n        buy_price = 0\n        #Runs through each kline\n        for i in range(len(klines)):\n            if point_finder > len(strategy_result)-1:\n                break\n            #If timestamp is in the interval, check if strategy has triggered a buy or sell\n            if time[i] > self.startTime and time[i] < self.endTime:\n                if(time[i] == strategy_result[point_finder][0]):\n                    if strategy_result[point_finder][3] == 'BUY':\n                        active_buy = True\n                        buy_price = float(strategy_result[point_finder][4])\n                        self.trades.append(['BUY', buy_price])\n                    if strategy_result[point_finder][3] == 'SELL' and active_buy == True:\n                        active_buy = False\n                        bought_amount = amount / buy_price\n                        self.num_trades += 1\n                        if(float(strategy_result[point_finder][4]) > buy_price):\n                            self.profitable_trades += 1\n                        amount = bought_amount * float(strategy_result[point_finder][4])\n                        self.trades.append(['SELL', float(strategy_result[point_finder][4])])\n                    point_finder += 1\n        self.amount = amount\n\n    '''\n    Prints the results of the backtest\n    '''\n    def printResults(self):\n        print(\"Trading Pair: \" + self.pair)\n        print(\"Interval: \" + self.interval)\n        print(\"Ending amount: \" + str(self.amount))\n        print(\"Number of Trades: \" + str(self.num_trades))\n        profitable = self.profitable_trades / self.num_trades * 100\n        print(\"Percentage of Profitable Trades: \" + str(profitable) + \"%\")\n        percent = self.amount / self.start * 100\n        print(str(percent) + \"% of starting amount\")\n        for entry in self.trades:\n            print(entry[0] + \" at \" + str(entry[1]))\n\n\n\nfilename = 'credentials.txt'\ntrader = Trader(filename)\ntrading_pair = 'BTCUSDT'\ninterval = '1d'\nklines = trader.client.get_klines(symbol=trading_pair,interval=interval)\nmacd_strategy = Strategy('MACD','CROSS',trading_pair,interval,klines)\n#macd_strategy.plotIndicator()\ntime = macd_strategy.getTime()\nmacd_backtest = Backtest(10000, time[0], time[len(time)-1], macd_strategy)\n\n\n```\nAnd the output of running the program becomes:\n```\nTrading Pair: BTCUSDT\nInterval: 1d\nEnding amount: 27000.907633194856\nNumber of Trades: 8\nPercentage of Profitable Trades: 62.5%\n270.00907633194856% of starting amount\nBUY at 4193.0\nSELL at 5477.03\nBUY at 6463.0\nSELL at 6506.98\nBUY at 7699.19\nSELL at 16488.98\nBUY at 17069.79\nSELL at 14400.0\nBUY at 11879.95\nSELL at 10237.51\nBUY at 8063.88\nSELL at 9271.64\nBUY at 8898.03\nSELL at 7795.51\nBUY at 7018.0\nSELL at 9187.56\n```\nNot bad for a beginner strategy, but then again, it was pretty difficult to lose money when investing in Bitcoin back in October, and a 62.5% profitable trade rate is far from desirable. Let’s experiment with a few different trading pairs, trading intervals, and date ranges and look at the results:\n```\nTrading Pair: BTCUSDT\nInterval: 1h\nEnding amount: 9587.746001592644\nNumber of Trades: 41\nPercentage of Profitable Trades: 36.58536585365854\n95.87746001592645% of starting amount\nBUY at 6887.99\nSELL at 6923.99\nBUY at 7099.85\nSELL at 6980.0\nBUY at 7120.0\nSELL at 6782.0\nBUY at 6740.11\nSELL at 6820.03\nBUY at 6851.97\nSELL at 6776.4\nBUY at 7690.0\nSELL at 7832.99\nBUY at 8193.49\nSELL at 8096.0\nBUY at 8077.96\nSELL at 7999.01\nBUY at 8067.87\nSELL at 8295.99\nBUY at 8350.0\nSELL at 8321.37\nBUY at 8017.0\nSELL at 7945.17\nBUY at 8125.0\nSELL at 8166.97\nBUY at 8230.89\nSELL at 8239.02\nBUY at 8259.08\nSELL at 8249.95\nBUY at 8350.56\nSELL at 8240.99\nBUY at 8488.01\nSELL at 8705.01\nBUY at 8940.03\nSELL at 8853.97\nBUY at 8942.39\nSELL at 8911.54\nBUY at 8934.01\nSELL at 9347.99\nBUY at 8841.0\nSELL at 8720.5\nBUY at 8845.02\nSELL at 9288.88\nBUY at 9163.03\nSELL at 9285.47\nBUY at 9405.45\nSELL at 9275.0\nBUY at 9318.06\nSELL at 9218.39\nBUY at 9039.0\nSELL at 8896.63\nBUY at 8999.99\nSELL at 9210.09\nBUY at 9471.05\nSELL at 9649.12\nBUY at 9839.99\nSELL at 9811.84\nBUY at 9862.04\nSELL at 9902.0\nBUY at 9652.75\nSELL at 9350.0\nBUY at 9362.01\nSELL at 9295.0\nBUY at 9190.0\nSELL at 9066.0\nBUY at 9112.24\nSELL at 9292.17\nBUY at 9369.0\nSELL at 9340.0\nBUY at 8606.93\nSELL at 8422.99\nBUY at 8450.01\nSELL at 8264.68\nBUY at 8311.0\nSELL at 8230.0\nBUY at 8501.83\nSELL at 8614.99\nBUY at 8680.0\nSELL at 8657.31\nBUY at 8762.18\nSELL at 8740.75\nBUY at 8350.26\nSELL at 8243.51\n```\n```\nTrading Pair: ETHUSDT\nInterval: 15m\nEnding amount: 10063.292422919483\nNumber of Trades: 36\nPercentage of Profitable Trades: 38.88888888888889%\n100.63292422919483% of starting amount\nBUY at 732.88\nSELL at 751.42\nBUY at 762.15\nSELL at 769.15\nBUY at 739.92\nSELL at 745.67\nBUY at 724.0\nSELL at 742.5\nBUY at 758.5\nSELL at 753.33\nBUY at 753.0\nSELL at 750.91\nBUY at 755.3\nSELL at 754.75\nBUY at 759.95\nSELL at 762.01\nBUY at 765.55\nSELL at 762.0\nBUY at 764.95\nSELL at 762.0\nBUY at 765.9\nSELL at 762.43\nBUY at 764.33\nSELL at 752.82\nBUY at 737.65\nSELL at 729.58\nBUY at 728.95\nSELL at 723.98\nBUY at 679.46\nSELL at 672.03\nBUY at 677.74\nSELL at 677.78\nBUY at 648.53\nSELL at 640.05\nBUY at 648.6\nSELL at 666.55\nBUY at 665.83\nSELL at 672.77\nBUY at 679.0\nSELL at 680.0\nBUY at 677.21\nSELL at 697.79\nBUY at 702.7\nSELL at 718.0\nBUY at 704.99\nSELL at 698.37\nBUY at 725.91\nSELL at 731.95\nBUY at 731.02\nSELL at 727.38\nBUY at 727.94\nSELL at 729.19\nBUY at 735.82\nSELL at 727.38\nBUY at 725.43\nSELL at 716.93\nBUY at 713.48\nSELL at 703.33\nBUY at 706.95\nSELL at 692.15\nBUY at 687.63\nSELL at 686.57\nBUY at 699.67\nSELL at 694.42\nBUY at 697.6\nSELL at 694.05\nBUY at 698.68\nSELL at 710.6\nBUY at 700.54\nSELL at 696.84\nBUY at 700.55\nSELL at 695.1\n```\n\nThese look a little more as expected from a beginner strategy. No need to panic just yet though, we are far from being done with our trading strategy development. \n\nMoving forward it would be nice to have a stable backtesting metric to compare strategies against each other. The metric that I’ll most commonly be using to compare strategies is avg. daily percent gain for a strategy on the 15 minute interval over the last 100 days. To do this we’ll need to pull some older data, which can be done using the get_historical_klines function in the Binance API. After we add our RSI indicator, we will test it using this metric in the next issue.\n\n# Step 4 – Adding RSI Strategy\nThe relative strength index (RSI) is a momentum indicator developed by noted technical analyst Welles Wilder, that compares the magnitude of recent gains and losses over a specified time period to measure speed and change of price movements of a security. It is primarily used to attempt to identify overbought or oversold conditions in the trading of an asset (definition from [Investopedia]( https://www.investopedia.com/terms/r/rsi.asp). After our earlier code cleanup, adding a function for this new indicator is quite simple, the calculateIndicator function becomes:\n```python3.6\nif self.indicator == 'MACD':\n    close = [float(entry[4]) for entry in self.klines]\n    close_array = np.asarray(close)\n\n    macd, macdsignal, macdhist = ta.MACD(close_array, fastperiod=12, slowperiod=26, signalperiod=9)\n    return [macd, macdsignal, macdhist]\n\nelif self.indicator == 'RSI':\n    close = [float(entry[4]) for entry in self.klines]\n    close_array = np.asarray(close)\n\n    rsi = ta.RSI(close_array, timeperiod=14)\n    return rsi\n\nelse:\n    return None\n\n```\ncalculateStrategy is:\n```python3.6\nif self.indicator == 'MACD':\n\n    if self.strategy == 'CROSS':\n        open_time = [int(entry[0]) for entry in self.klines]\n        new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n        self.time = new_time\n        crosses = []\n        macdabove = False\n        #Runs through each timestamp in order\n        for i in range(len(self.indicator_result[0])):\n            if np.isnan(self.indicator_result[0][i]) or np.isnan(self.indicator_result[1][i]):\n                pass\n            #If both the MACD and signal are well defined, we compare the 2 and decide if a cross has occured\n            else:\n                if self.indicator_result[0][i] > self.indicator_result[1][i]:\n                    if macdabove == False:\n                        macdabove = True\n                        #Appends the timestamp, MACD value at the timestamp, color of dot, buy signal, and the buy price\n                        cross = [new_time[i],self.indicator_result[0][i] , 'go', 'BUY', self.klines[i][4]]\n                        crosses.append(cross)\n                else:\n                    if macdabove == True:\n                        macdabove = False\n                        #Appends the timestamp, MACD value at the timestamp, color of dot, sell signal, and the sell price\n                        cross = [new_time[i], self.indicator_result[0][i], 'ro', 'SELL', self.klines[i][4]]\n                        crosses.append(cross)\n        return crosses\n\n    else:\n        return None\nelif self.indicator == 'RSI':\n    if self.strategy == '7030':\n        open_time = [int(entry[0]) for entry in self.klines]\n        new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n        self.time = new_time\n        result = []\n        active_buy = False\n        # Runs through each timestamp in order\n        for i in range(len(self.indicator_result)):\n            if np.isnan(self.indicator_result[i]):\n                pass\n            # If the RSI is well defined, check if over 70 or under 30\n            else:\n                if float(self.indicator_result[i]) < 30 and active_buy == False:\n                    # Appends the timestamp, RSI value at the timestamp, color of dot, buy signal, and the buy price\n                    entry = [new_time[i], self.indicator_result[i], 'go', 'BUY', self.klines[i][4]]\n                    result.append(entry)\n                    active_buy = True\n                elif float(self.indicator_result[i]) > 70 and active_buy == True:\n                    # Appends the timestamp, RSI value at the timestamp, color of dot, sell signal, and the sell price\n                    entry = [new_time[i], self.indicator_result[i], 'ro', 'SELL', self.klines[i][4]]\n                    result.append(entry)\n                    active_buy = False\n        return result\n    elif self.strategy == '8020':\n        open_time = [int(entry[0]) for entry in self.klines]\n        new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n        self.time = new_time\n        result = []\n        active_buy = False\n        # Runs through each timestamp in order\n        for i in range(len(self.indicator_result)):\n            if np.isnan(self.indicator_result[i]):\n                pass\n            # If the RSI is well defined, check if over 80 or under 20\n            else:\n                if float(self.indicator_result[i]) < 20 and active_buy == False:\n                    # Appends the timestamp, RSI value at the timestamp, color of dot, buy signal, and the buy price\n                    entry = [new_time[i], self.indicator_result[i], 'go', 'BUY', self.klines[i][4]]\n                    result.append(entry)\n                    active_buy = True\n                elif float(self.indicator_result[i]) > 80 and active_buy == True:\n                    # Appends the timestamp, RSI value at the timestamp, color of dot, sell signal, and the sell price\n                    entry = [new_time[i], self.indicator_result[i], 'ro', 'SELL', self.klines[i][4]]\n                    result.append(entry)\n                    active_buy = False\n        return result\n\n\nelse:\n    return None\n\n```\nAnd plotIndicator is:\n```python3.6\nopen_time = [int(entry[0]) for entry in klines]\nnew_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\nplt.style.use('dark_background')\nfor entry in self.strategy_result:\n    plt.plot(entry[0], entry[1], entry[2])\nif self.indicator == 'MACD':\n    plt.plot(new_time, self.indicator_result[0], label='MACD')\n    plt.plot(new_time, self.indicator_result[1], label='MACD Signal')\n    plt.plot(new_time, self.indicator_result[2], label='MACD Histogram')\n\nelif self.indicator == 'RSI':\n    plt.plot(new_time, self.indicator_result, label='RSI')\n\nelse:\n    pass\n\ntitle = self.indicator + \" Plot for \" + self.pair + \" on \" + self.interval\nplt.title(title)\nplt.xlabel(\"Open Time\")\nplt.ylabel(\"Value\")\nplt.legend()\nplt.show()\n\n```\n\nWe have implemented 2 RSI trading strategies, one with the bounds of 70 and 30, and the other with bounds of 80 and 20. There are different situations where each of these bounds are useful so we have implemented both. Due to the length of this post, we will be stopping here for now. In the next installment we’ll be testing out the RSI strategy making adjustments if necessary, combining the MACD and RSI strategies, adding stop losses, and setting up our bot to run in real time.\n\nAs always, any questions, comments, concerns, general feedback, or statements of well being are always appreciated.\n\nThanks for reading,\nAndrew",
      "json_metadata": "{\"tags\":[\"cryptocurrency\",\"trading\",\"bot\",\"money\"],\"links\":[\"https://www.investopedia.com/terms/r/rsi.asp\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "cryptocurrency",
      "permlink": "part-3-adding-backtesting-and-support-for-rsi-indicator",
      "title": "Cyptocurrency Trading Bot Part 3 - Adding Backtesting and Support for RSI Indicator"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-05-18T17:33:30",
  "trx_id": "531287f94927db44fe33817e747c9818f9787725",
  "trx_in_block": 42,
  "virtual_op": 0
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  "block": 22544146,
  "op": [
    "comment",
    {
      "author": "genesiscrypto",
      "body": "# Step 1 – Code Clean Up\nAfter the previous part, we have some functional code to give us a look at the MACD indicator for a particular trading pair, but the code is not particularly useful for a number of reasons. If we wanted to add additional indicators or strategies, the duplicate code would stack up very quickly, not practical. We’ll instead create a new Strategy class which will handle all of the indicator calculation, strategy calculation (finding buy and sell points), and plotting. With this change our complete code now becomes:\n```python3.6\nfrom binance.client import Client\nimport talib as ta\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom datetime import datetime\n\nclass Trader:\n    def __init__(self, file):\n        self.connect(file)\n\n    \"\"\" Creates Binance client \"\"\"\n    def connect(self,file):\n        lines = [line.rstrip('\\n') for line in open(file)]\n        key = lines[0]\n        secret = lines[1]\n        self.client = Client(key, secret)\n\n    \"\"\" Gets all account balances \"\"\"\n    def getBalances(self):\n        prices = self.client.get_withdraw_history()\n        return prices\n\nclass Strategy:\n\n    def __init__(self, indicator_name, strategy_name, pair, interval, klines):\n        self.indicator = indicator_name\n        self.strategy = strategy_name\n        self.pair = pair\n        self.interval = interval\n        self.klines = klines\n        self.indicator_result = self.calculateIndicator()\n        self.strategy_result = self.calculateStrategy()\n\n\n    def calculateIndicator(self):\n        if self.indicator == 'MACD':\n            close = [float(entry[4]) for entry in self.klines]\n            close_array = np.asarray(close)\n\n            macd, macdsignal, macdhist = ta.MACD(close_array, fastperiod=12, slowperiod=26, signalperiod=9)\n            return [macd, macdsignal, macdhist]\n\n        else:\n            return None\n\n\n    def calculateStrategy(self):\n        if self.indicator == 'MACD':\n            if self.strategy == 'CROSS':\n                open_time = [int(entry[0]) for entry in self.klines]\n                new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n                crosses = []\n                macdabove = False\n                for i in range(len(self.indicator_result[0])):\n                    if np.isnan(self.indicator_result[0][i]) or np.isnan(self.indicator_result[1][i]):\n                        pass\n                    else:\n                        if self.indicator_result[0][i] > self.indicator_result[1][i]:\n                            if macdabove == False:\n                                macdabove = True\n                                cross = [new_time[i],self.indicator_result[0][i] , 'go']\n                                crosses.append(cross)\n                        else:\n                            if macdabove == True:\n                                macdabove = False\n                                cross = [new_time[i], self.indicator_result[0][i], 'ro']\n                                crosses.append(cross)\n                return crosses\n\n            else:\n                return None\n        else:\n            return None\n\n\n\n    def plotIndicator(self):\n        if self.indicator == 'MACD':\n            open_time = [int(entry[0]) for entry in klines]\n            new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n            plt.style.use('dark_background')\n            plt.plot(new_time, self.indicator_result[0], label='MACD')\n            plt.plot(new_time, self.indicator_result[1], label='MACD Signal')\n            plt.plot(new_time, self.indicator_result[2], label='MACD Histogram')\n            for entry in self.strategy_result:\n                plt.plot(entry[0], entry[1], entry[2])\n            title = \"MACD Plot for \" + self.pair + \" on \" + self.interval\n            plt.title(title)\n            plt.xlabel(\"Open Time\")\n            plt.ylabel(\"Value\")\n            plt.legend()\n            plt.show()\n\n        else:\n            pass\n\n\n\n\nfilename = 'credentials.txt'\ntrader = Trader(filename)\ntrading_pair = 'BTCUSDT'\ninterval = '1d'\nklines = trader.client.get_klines(symbol=trading_pair,interval=interval)\nmacd_strategy = Strategy('MACD','CROSS',trading_pair,interval,klines)\nmacd_strategy.plotIndicator()\n\n\n```\n\nThis is much more efficient, and will for us to expand the functionality of our script with ease in the future.\n\n# Step 2 – Backtesting\nMoving forward, backtesting will be a crucial part of this project, and it starts right here. We’re going to create a simple backtesting class with init parameters for starting amount and a strategy object, and an output with ending amount, percent change, number of trades, percentage of profitable trades, and a detailed report of each trade executed. After adding this functionality (and some more comments for readability), our code looks like this:\n```python3.6\nfrom binance.client import Client\nimport talib as ta\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom datetime import datetime\n\nclass Trader:\n    def __init__(self, file):\n        self.connect(file)\n\n    \"\"\" Creates Binance client \"\"\"\n    def connect(self,file):\n        lines = [line.rstrip('\\n') for line in open(file)]\n        key = lines[0]\n        secret = lines[1]\n        self.client = Client(key, secret)\n\n    \"\"\" Gets all account balances \"\"\"\n    def getBalances(self):\n        prices = self.client.get_withdraw_history()\n        return prices\n\nclass Strategy:\n\n    def __init__(self, indicator_name, strategy_name, pair, interval, klines):\n        #Name of indicator\n        self.indicator = indicator_name\n        #Name of strategy being used\n        self.strategy = strategy_name\n        #Trading pair\n        self.pair = pair\n        #Trading interval\n        self.interval = interval\n        #Kline data for the pair on given interval\n        self.klines = klines\n        #Calculates the indicator\n        self.indicator_result = self.calculateIndicator()\n        #Uses the indicator to run strategy\n        self.strategy_result = self.calculateStrategy()\n\n\n    '''\n    Calculates the desired indicator given the init parameters\n    '''\n    def calculateIndicator(self):\n        if self.indicator == 'MACD':\n            close = [float(entry[4]) for entry in self.klines]\n            close_array = np.asarray(close)\n\n            macd, macdsignal, macdhist = ta.MACD(close_array, fastperiod=12, slowperiod=26, signalperiod=9)\n            return [macd, macdsignal, macdhist]\n\n        else:\n            return None\n\n\n    '''\n    Runs the desired strategy given the indicator results\n    '''\n    def calculateStrategy(self):\n        if self.indicator == 'MACD':\n\n            if self.strategy == 'CROSS':\n                open_time = [int(entry[0]) for entry in self.klines]\n                new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n                self.time = new_time\n                crosses = []\n                macdabove = False\n                #Runs through each timestamp in order\n                for i in range(len(self.indicator_result[0])):\n                    if np.isnan(self.indicator_result[0][i]) or np.isnan(self.indicator_result[1][i]):\n                        pass\n                    #If both the MACD and signal are well defined, we compare the 2 and decide if a cross has occured\n                    else:\n                        if self.indicator_result[0][i] > self.indicator_result[1][i]:\n                            if macdabove == False:\n                                macdabove = True\n                                #Appends the timestamp, MACD value at the timestamp, color of dot, buy signal, and the buy price\n                                cross = [new_time[i],self.indicator_result[0][i] , 'go', 'BUY', self.klines[i][4]]\n                                crosses.append(cross)\n                        else:\n                            if macdabove == True:\n                                macdabove = False\n                                #Appends the timestamp, MACD value at the timestamp, color of dot, sell signal, and the sell price\n                                cross = [new_time[i], self.indicator_result[0][i], 'ro', 'SELL', self.klines[i][4]]\n                                crosses.append(cross)\n                return crosses\n\n            else:\n                return None\n        else:\n            return None\n\n    '''\n    Getter for the strategy result\n    '''\n    def getStrategyResult(self):\n        return self.strategy_result\n\n    '''\n    Getter for the klines\n    '''\n    def getKlines(self):\n        return self.klines\n\n    '''\n    Getter for the trading pair\n    '''\n    def getPair(self):\n        return self.pair\n\n    '''\n    Getter for the trading interval\n    '''\n    def getInterval(self):\n        return self.interval\n\n    '''\n    Getter for the time list\n    '''\n    def getTime(self):\n        return self.time\n\n    '''\n    Plots the desired indicator with strategy buy and sell points\n    '''\n    def plotIndicator(self):\n        if self.indicator == 'MACD':\n            open_time = [int(entry[0]) for entry in klines]\n            new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n            plt.style.use('dark_background')\n            plt.plot(new_time, self.indicator_result[0], label='MACD')\n            plt.plot(new_time, self.indicator_result[1], label='MACD Signal')\n            plt.plot(new_time, self.indicator_result[2], label='MACD Histogram')\n            for entry in self.strategy_result:\n                plt.plot(entry[0], entry[1], entry[2])\n            title = \"MACD Plot for \" + self.pair + \" on \" + self.interval\n            plt.title(title)\n            plt.xlabel(\"Open Time\")\n            plt.ylabel(\"Value\")\n            plt.legend()\n            plt.show()\n\n        else:\n            pass\n\nclass Backtest:\n    def __init__(self, starting_amount, start_datetime, end_datetime, strategy):\n        #Starting amount\n        self.start = starting_amount\n        #Number of trades\n        self.num_trades = 0\n        #Number of profitable trades\n        self.profitable_trades = 0\n        #Running amount\n        self.amount = self.start\n        #Start of desired interval\n        self.startTime = start_datetime\n        #End of desired interval\n        self.endTime = end_datetime\n        #Strategy object\n        self.strategy = strategy\n        #Trading pair\n        self.pair = self.strategy.getPair()\n        #Trading interval\n        self.interval = self.strategy.getInterval()\n        #Outputs the trades exectued\n        self.trades = []\n        #Runs the backtest\n        self.results = self.runBacktest()\n        #Prints the results\n        self.printResults()\n\n\n    def runBacktest(self):\n        amount = self.start\n        klines = self.strategy.getKlines()\n        time = self.strategy.getTime()\n        point_finder = 0\n        strategy_result = self.strategy.getStrategyResult()\n        #Finds the first cross point within the desired backtest interval\n        while strategy_result[point_finder][0] < self.startTime:\n            point_finder += 1\n        #Initialize to not buy\n        active_buy = False\n        buy_price = 0\n        #Runs through each kline\n        for i in range(len(klines)):\n            if point_finder > len(strategy_result)-1:\n                break\n            #If timestamp is in the interval, check if strategy has triggered a buy or sell\n            if time[i] > self.startTime and time[i] < self.endTime:\n                if(time[i] == strategy_result[point_finder][0]):\n                    if strategy_result[point_finder][3] == 'BUY':\n                        active_buy = True\n                        buy_price = float(strategy_result[point_finder][4])\n                        self.trades.append(['BUY', buy_price])\n                    if strategy_result[point_finder][3] == 'SELL' and active_buy == True:\n                        active_buy = False\n                        bought_amount = amount / buy_price\n                        self.num_trades += 1\n                        if(float(strategy_result[point_finder][4]) > buy_price):\n                            self.profitable_trades += 1\n                        amount = bought_amount * float(strategy_result[point_finder][4])\n                        self.trades.append(['SELL', float(strategy_result[point_finder][4])])\n                    point_finder += 1\n        self.amount = amount\n\n    '''\n    Prints the results of the backtest\n    '''\n    def printResults(self):\n        print(\"Trading Pair: \" + self.pair)\n        print(\"Interval: \" + self.interval)\n        print(\"Ending amount: \" + str(self.amount))\n        print(\"Number of Trades: \" + str(self.num_trades))\n        profitable = self.profitable_trades / self.num_trades * 100\n        print(\"Percentage of Profitable Trades: \" + str(profitable) + \"%\")\n        percent = self.amount / self.start * 100\n        print(str(percent) + \"% of starting amount\")\n        for entry in self.trades:\n            print(entry[0] + \" at \" + str(entry[1]))\n\n\n\nfilename = 'credentials.txt'\ntrader = Trader(filename)\ntrading_pair = 'BTCUSDT'\ninterval = '1d'\nklines = trader.client.get_klines(symbol=trading_pair,interval=interval)\nmacd_strategy = Strategy('MACD','CROSS',trading_pair,interval,klines)\n#macd_strategy.plotIndicator()\ntime = macd_strategy.getTime()\nmacd_backtest = Backtest(10000, time[0], time[len(time)-1], macd_strategy)\n\n\n```\nAnd the output of running the program becomes:\n```\nTrading Pair: BTCUSDT\nInterval: 1d\nEnding amount: 27000.907633194856\nNumber of Trades: 8\nPercentage of Profitable Trades: 62.5%\n270.00907633194856% of starting amount\nBUY at 4193.0\nSELL at 5477.03\nBUY at 6463.0\nSELL at 6506.98\nBUY at 7699.19\nSELL at 16488.98\nBUY at 17069.79\nSELL at 14400.0\nBUY at 11879.95\nSELL at 10237.51\nBUY at 8063.88\nSELL at 9271.64\nBUY at 8898.03\nSELL at 7795.51\nBUY at 7018.0\nSELL at 9187.56\n```\nNot bad for a beginner strategy, but then again, it was pretty difficult to lose money when investing in Bitcoin back in October, and a 62.5% profitable trade rate is far from desirable. Let’s experiment with a few different trading pairs, trading intervals, and date ranges and look at the results:\n```\nTrading Pair: BTCUSDT\nInterval: 1h\nEnding amount: 9587.746001592644\nNumber of Trades: 41\nPercentage of Profitable Trades: 36.58536585365854\n95.87746001592645% of starting amount\nBUY at 6887.99\nSELL at 6923.99\nBUY at 7099.85\nSELL at 6980.0\nBUY at 7120.0\nSELL at 6782.0\nBUY at 6740.11\nSELL at 6820.03\nBUY at 6851.97\nSELL at 6776.4\nBUY at 7690.0\nSELL at 7832.99\nBUY at 8193.49\nSELL at 8096.0\nBUY at 8077.96\nSELL at 7999.01\nBUY at 8067.87\nSELL at 8295.99\nBUY at 8350.0\nSELL at 8321.37\nBUY at 8017.0\nSELL at 7945.17\nBUY at 8125.0\nSELL at 8166.97\nBUY at 8230.89\nSELL at 8239.02\nBUY at 8259.08\nSELL at 8249.95\nBUY at 8350.56\nSELL at 8240.99\nBUY at 8488.01\nSELL at 8705.01\nBUY at 8940.03\nSELL at 8853.97\nBUY at 8942.39\nSELL at 8911.54\nBUY at 8934.01\nSELL at 9347.99\nBUY at 8841.0\nSELL at 8720.5\nBUY at 8845.02\nSELL at 9288.88\nBUY at 9163.03\nSELL at 9285.47\nBUY at 9405.45\nSELL at 9275.0\nBUY at 9318.06\nSELL at 9218.39\nBUY at 9039.0\nSELL at 8896.63\nBUY at 8999.99\nSELL at 9210.09\nBUY at 9471.05\nSELL at 9649.12\nBUY at 9839.99\nSELL at 9811.84\nBUY at 9862.04\nSELL at 9902.0\nBUY at 9652.75\nSELL at 9350.0\nBUY at 9362.01\nSELL at 9295.0\nBUY at 9190.0\nSELL at 9066.0\nBUY at 9112.24\nSELL at 9292.17\nBUY at 9369.0\nSELL at 9340.0\nBUY at 8606.93\nSELL at 8422.99\nBUY at 8450.01\nSELL at 8264.68\nBUY at 8311.0\nSELL at 8230.0\nBUY at 8501.83\nSELL at 8614.99\nBUY at 8680.0\nSELL at 8657.31\nBUY at 8762.18\nSELL at 8740.75\nBUY at 8350.26\nSELL at 8243.51\n```\n```\nTrading Pair: ETHUSDT\nInterval: 15m\nEnding amount: 10063.292422919483\nNumber of Trades: 36\nPercentage of Profitable Trades: 38.88888888888889%\n100.63292422919483% of starting amount\nBUY at 732.88\nSELL at 751.42\nBUY at 762.15\nSELL at 769.15\nBUY at 739.92\nSELL at 745.67\nBUY at 724.0\nSELL at 742.5\nBUY at 758.5\nSELL at 753.33\nBUY at 753.0\nSELL at 750.91\nBUY at 755.3\nSELL at 754.75\nBUY at 759.95\nSELL at 762.01\nBUY at 765.55\nSELL at 762.0\nBUY at 764.95\nSELL at 762.0\nBUY at 765.9\nSELL at 762.43\nBUY at 764.33\nSELL at 752.82\nBUY at 737.65\nSELL at 729.58\nBUY at 728.95\nSELL at 723.98\nBUY at 679.46\nSELL at 672.03\nBUY at 677.74\nSELL at 677.78\nBUY at 648.53\nSELL at 640.05\nBUY at 648.6\nSELL at 666.55\nBUY at 665.83\nSELL at 672.77\nBUY at 679.0\nSELL at 680.0\nBUY at 677.21\nSELL at 697.79\nBUY at 702.7\nSELL at 718.0\nBUY at 704.99\nSELL at 698.37\nBUY at 725.91\nSELL at 731.95\nBUY at 731.02\nSELL at 727.38\nBUY at 727.94\nSELL at 729.19\nBUY at 735.82\nSELL at 727.38\nBUY at 725.43\nSELL at 716.93\nBUY at 713.48\nSELL at 703.33\nBUY at 706.95\nSELL at 692.15\nBUY at 687.63\nSELL at 686.57\nBUY at 699.67\nSELL at 694.42\nBUY at 697.6\nSELL at 694.05\nBUY at 698.68\nSELL at 710.6\nBUY at 700.54\nSELL at 696.84\nBUY at 700.55\nSELL at 695.1\n```\n\nThese look a little more as expected from a beginner strategy. No need to panic just yet though, we are far from being done with our trading strategy development. \n\nMoving forward it would be nice to have a stable backtesting metric to compare strategies against each other. The metric that I’ll most commonly be using to compare strategies is avg. daily percent gain for a strategy on the 15 minute interval over the last 100 days. To do this we’ll need to pull some older data, which can be done using the get_historical_klines function in the Binance API. After we add our RSI indicator, we will test it using this metric in the next issue.\n\n# Step 4 – Adding RSI Strategy\nThe relative strength index (RSI) is a momentum indicator developed by noted technical analyst Welles Wilder, that compares the magnitude of recent gains and losses over a specified time period to measure speed and change of price movements of a security. It is primarily used to attempt to identify overbought or oversold conditions in the trading of an asset (definition from [Investopedia]( https://www.investopedia.com/terms/r/rsi.asp). After our earlier code cleanup, adding a function for this new indicator is quite simple, the calculateIndicator function becomes:\n```python3.6\nif self.indicator == 'MACD':\n    close = [float(entry[4]) for entry in self.klines]\n    close_array = np.asarray(close)\n\n    macd, macdsignal, macdhist = ta.MACD(close_array, fastperiod=12, slowperiod=26, signalperiod=9)\n    return [macd, macdsignal, macdhist]\n\nelif self.indicator == 'RSI':\n    close = [float(entry[4]) for entry in self.klines]\n    close_array = np.asarray(close)\n\n    rsi = ta.RSI(close_array, timeperiod=14)\n    return rsi\n\nelse:\n    return None\n\n```\ncalculateStrategy is:\n```python3.6\nif self.indicator == 'MACD':\n\n    if self.strategy == 'CROSS':\n        open_time = [int(entry[0]) for entry in self.klines]\n        new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n        self.time = new_time\n        crosses = []\n        macdabove = False\n        #Runs through each timestamp in order\n        for i in range(len(self.indicator_result[0])):\n            if np.isnan(self.indicator_result[0][i]) or np.isnan(self.indicator_result[1][i]):\n                pass\n            #If both the MACD and signal are well defined, we compare the 2 and decide if a cross has occured\n            else:\n                if self.indicator_result[0][i] > self.indicator_result[1][i]:\n                    if macdabove == False:\n                        macdabove = True\n                        #Appends the timestamp, MACD value at the timestamp, color of dot, buy signal, and the buy price\n                        cross = [new_time[i],self.indicator_result[0][i] , 'go', 'BUY', self.klines[i][4]]\n                        crosses.append(cross)\n                else:\n                    if macdabove == True:\n                        macdabove = False\n                        #Appends the timestamp, MACD value at the timestamp, color of dot, sell signal, and the sell price\n                        cross = [new_time[i], self.indicator_result[0][i], 'ro', 'SELL', self.klines[i][4]]\n                        crosses.append(cross)\n        return crosses\n\n    else:\n        return None\nelif self.indicator == 'RSI':\n    if self.strategy == '7030':\n        open_time = [int(entry[0]) for entry in self.klines]\n        new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n        self.time = new_time\n        result = []\n        active_buy = False\n        # Runs through each timestamp in order\n        for i in range(len(self.indicator_result)):\n            if np.isnan(self.indicator_result[i]):\n                pass\n            # If the RSI is well defined, check if over 70 or under 30\n            else:\n                if float(self.indicator_result[i]) < 30 and active_buy == False:\n                    # Appends the timestamp, RSI value at the timestamp, color of dot, buy signal, and the buy price\n                    entry = [new_time[i], self.indicator_result[i], 'go', 'BUY', self.klines[i][4]]\n                    result.append(entry)\n                    active_buy = True\n                elif float(self.indicator_result[i]) > 70 and active_buy == True:\n                    # Appends the timestamp, RSI value at the timestamp, color of dot, sell signal, and the sell price\n                    entry = [new_time[i], self.indicator_result[i], 'ro', 'SELL', self.klines[i][4]]\n                    result.append(entry)\n                    active_buy = False\n        return result\n    elif self.strategy == '8020':\n        open_time = [int(entry[0]) for entry in self.klines]\n        new_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\n        self.time = new_time\n        result = []\n        active_buy = False\n        # Runs through each timestamp in order\n        for i in range(len(self.indicator_result)):\n            if np.isnan(self.indicator_result[i]):\n                pass\n            # If the RSI is well defined, check if over 80 or under 20\n            else:\n                if float(self.indicator_result[i]) < 20 and active_buy == False:\n                    # Appends the timestamp, RSI value at the timestamp, color of dot, buy signal, and the buy price\n                    entry = [new_time[i], self.indicator_result[i], 'go', 'BUY', self.klines[i][4]]\n                    result.append(entry)\n                    active_buy = True\n                elif float(self.indicator_result[i]) > 80 and active_buy == True:\n                    # Appends the timestamp, RSI value at the timestamp, color of dot, sell signal, and the sell price\n                    entry = [new_time[i], self.indicator_result[i], 'ro', 'SELL', self.klines[i][4]]\n                    result.append(entry)\n                    active_buy = False\n        return result\n\n\nelse:\n    return None\n\n```\nAnd plotIndicator is:\n```python3.6\nopen_time = [int(entry[0]) for entry in klines]\nnew_time = [datetime.fromtimestamp(time / 1000) for time in open_time]\nplt.style.use('dark_background')\nfor entry in self.strategy_result:\n    plt.plot(entry[0], entry[1], entry[2])\nif self.indicator == 'MACD':\n    plt.plot(new_time, self.indicator_result[0], label='MACD')\n    plt.plot(new_time, self.indicator_result[1], label='MACD Signal')\n    plt.plot(new_time, self.indicator_result[2], label='MACD Histogram')\n\nelif self.indicator == 'RSI':\n    plt.plot(new_time, self.indicator_result, label='RSI')\n\nelse:\n    pass\n\ntitle = self.indicator + \" Plot for \" + self.pair + \" on \" + self.interval\nplt.title(title)\nplt.xlabel(\"Open Time\")\nplt.ylabel(\"Value\")\nplt.legend()\nplt.show()\n\n```\n\nWe have implemented 2 RSI trading strategies, one with the bounds of 70 and 30, and the other with bounds of 80 and 20. There are different situations where each of these bounds are useful so we have implemented both. Due to the length of this post, we will be stopping here for now. In the next installment we’ll be testing out the RSI strategy making adjustments if necessary, combining the MACD and RSI strategies, adding stop losses, and setting up our bot to run in real time.\n\nAs always, any questions, comments, concerns, general feedback, or statements of well being are always appreciated.\n\nThanks for reading,\nAndrew",
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      "body": "Yes, adding stop losses will definitely be a high priority, and will be even more helpful when trading on shorter intervals. I typically like to trade on the 15m and 1h intervals so a lot of the strategies I'll be posting moving forward will be centered around those.",
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      "body": "Do you plan on simulating stop losses / take profits on your strategies ?\nRight now I can see 9 signals out of 16 are good (for the strategy you presented), so you'll have to implement stop-losses. Or maybe hedging with leverages, I don't know which can be the most efficient.\nThere's a lot of sudden movements in these markets that can easily trigger a stop loss if too tight.\n\nWill you try some more short-term strategies too ?\n\nAnyway, looking forward to hear more about your tests !",
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      "author": "algo.coder",
      "body": "Great ! Looking forward to this then.\nDo you plan on using only classical indicators (MACD, RSI, etc.) with the data available on the Binance API (the kline endpoint) or will you also use more original data like maybe Twitter with sentiment analysis, Reddit, orderbooks (you won't have the history) or trades (you can get the history but it's 500 by 500 so it's gonna take some time with the call restrictions).\nI'm developing a bot to, that's why I'm interested in this.",
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      "body": "I'm planning on trying out a bunch of different strategies with a very strong emphasis on backtesting. Ultimately I'd like to dive into some machine learning and use TensorFlow to tune the weight of different indicators in deciding on trades.",
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      "body": "I can't tell you how many times I've started tutorials, failed on the setup stage, and given up, hence the \"written for dummies\" feel to the post. Glad you liked it!",
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      "body": "@@ -6990,16 +6990,206 @@\n ()%0A%60%60%60%0A%0A\n+After running this code, we get the following:%0A%0A%0A!%5Bcrossover.png%5D(https://steemitimages.com/DQmNmpYcvj7r459RFL7BMaNzKvauvNVKtQbN7bFGyY4zfCb/crossover.png)%0AFigure 4: MACD crossover strategy%0A%0A\n This cod\n",
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      "title": "Cryptocurrency Trading Bot Part 2 - TA-LIB Setup and Beginning Strategy"
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      "author": "genesiscrypto",
      "body": "# Where We Are Now?\n\nIf you have followed [Step 1](https://steemit.com/cryptocurrency/@genesiscrypto/cryptocurrency-trading-bot-part-1-setup-and-connect-to-binance), then you now have a Binance client setup in Python. The next logical step is to develop a strategy to teach our bot how to trade. The strategy that we will develop today will sadly not make you a millionaire, but utilizing the principles learned in this tutorial in combination with indicator strategies and back testing methods which we will develop in future tutorials just might, so this is an important first step for our bot.\n\n# Indicators\n\nThe key to building any good trading strategy starts with good indicators. There are thousands of them out there, and luckily for us, we won't have to reinvent the wheel to access them because we have access to historical data in the form of the Binance API, and we will soon have TA-LIB which will give us access to these indicators in the form of a handy Python library. \n\n# Step 1 - Installing TA-Lib and MatplotLib\nTA-Lib (short for Technical Analysis Library) can be installed by simply running the command:\n```\npip install TA-Lib\n```\nThis instillation is notorious for running into issues, but luckily many people have failed with this very same installation so if you have problems, check the troubleshooting section on the [Github page]( https://github.com/mrjbq7/ta-lib), or do a quick google search of any error produced by the installation  and you should find a solution.  \nWe will be using MatPlotLib to create plots and other analytics moving forward. MatPlotLib can be installed with:\n```\npip install matplotlib\n```\nNumpy is a helpful library for doing a number of necessary array and matrix operations among other things, and can be installed with:\n```\npip install numpy\n```\nThe final two libraries we will need are pandas and pandas_datareader. These libraries will simplify the task of converting data pulled from the Binance API into a format which TA-Lib can compute from. We will not use these libraries today for our simple example, but they will come in handy later. These libraries can be installed with:\n```\npip install pandas\npip install pandas_datareader\n```\n\n\n# Step 2 – Generate First Indicator \nNow we will use the data pulled from the Binance API in combination with TA-Lib, Numpy, and MatPlotLib to generate a MACD plot for the BTC/USDT trading pair. MACD stands for moving average convergence divergence. It is a trend-following momentum indicator that shows the relationship between two moving averages of prices. We will plot these two moving average lines with the following modified python script which we started in the previous part.\n```python3.6\nfrom binance.client import Client\nimport talib as ta\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom datetime import datetime\n\nclass Trader:\n    def __init__(self, file):\n        self.connect(file)\n\n    \"\"\" Creates Binance client \"\"\"\n    def connect(self,file):\n        lines = [line.rstrip('\\n') for line in open(file)]\n        key = lines[0]\n        secret = lines[1]\n        self.client = Client(key, secret)\n\n    \"\"\" Gets all account balances \"\"\"\n    def getBalances(self):\n        prices = self.client.get_withdraw_history()\n        return prices\n\nfilename = 'credentials.txt'\ntrader = Trader(filename)\n\ntrading_pair = 'BTCUSDT'\ninterval = '1d'\nklines = trader.client.get_klines(symbol=trading_pair,interval=interval)\nopen_time = [int(entry[0]) for entry in klines]\nclose = [float(entry[4]) for entry in klines]\nclose_array = np.asarray(close)\nnew_time = [datetime.fromtimestamp(time/1000) for time in open_time]\n\nmacd, macdsignal, macdhist = ta.MACD(close_array, fastperiod=12, slowperiod=26, signalperiod=9)\n\n\n\nplt.style.use('dark_background')\nplt.plot(new_time, macd, label='MACD')\nplt.plot(new_time, macdsignal, label='MACD Signal')\nplt.plot(new_time, macdhist, label='MACD Histogram')\nplt.title(\"MACD Plot for BTC/USDT\")\nplt.xlabel(\"Open Time\")\nplt.ylabel(\"Value\")\nplt.legend()\nplt.show()\n\n```\nIn future tutorials we will move code for data generation, indicator computing, and plotting into separate classes and functions to make it easier to build complex trading strategies, but for now this code will be fine at the module level.\n\nIf all of your libraries have been installed successfully, you should see an output that looks like this:\n\n![macd1.png](https://steemitimages.com/DQmQr6oJVTqFFij9gVNn46DtUN1o5tQZdip7MmgZzqUuych/macd1.png)\nFigure 1: MACD output from above code\n\nBy pulling up the MACD computed by [TradingView]( https://www.tradingview.com/chart/8qIyPK2R/), we can compare this to our isolated MACD line:\n![macd3.png](https://steemitimages.com/DQmbbamKiA8dvxh846U33PkSCvVFHZJCRdTCiqTjeTUtxVD/macd3.png)\nFigure 2: Isolated MACD Line from above code\n\n![macd2.png](https://steemitimages.com/DQmRnAm5TXvbSY27xGHFmXx4BUmnViEwbD5edcg2CWo9fbC/macd2.png)\nFigure 3: MACD Output From TradingView\n\nAn exact match, this means that we are now successfully able to compute the MACD indicator and can use it to build our very first trading strategy!\n\n# Step 3 – Crafting a Strategy\nThe strategy that we will be creating will be a simple MACD crosover trading strategy. The idea is fairly simple and looks at the relationship between the MACD line and the MACD signal line (also called the trigger line). There are two types of crossovers:\n* Bullish MACD Crossover – We have a bullish MACD crossover when the MACD line crosses the slower signal line in the bullish direction. This action generates a bullish signal on the chart, which implies that the price might start an increase.\n* Bearish MACD Crossover – The bearish MACD crossover is opposite to the bullish MACD crossover. When the MACD line crosses the signal line in the bearish direction, we have a bearish crossover. This hints that the price action might be entering a bearish move.\n\nTo start, we will add red and green indicators to our plot to show these crossover point. We can do this by replacing the code beneath this line:\n```python3.6\nmacd, macdsignal, macdhist = ta.MACD(close_array, fastperiod=12, slowperiod=26, signalperiod=9)\n```\nwith this:\n```python3.6\ncrosses = []\nmacdabove = False\nfor i in range(len(macd)):\n    if np.isnan(macd[i]) or np.isnan(macdsignal[i]):\n        pass\n    else:\n        if macd[i] > macdsignal[i]:\n            if macdabove == False:\n                macdabove = True\n                cross = [new_time[i],macd[i],'go']\n                crosses.append(cross)\n        else:\n            if macdabove == True:\n                macdabove = False\n                cross = [new_time[i],macd[i],'ro']\n                crosses.append(cross)\n\n\n\nplt.style.use('dark_background')\nplt.plot(new_time, macd, label='MACD')\nplt.plot(new_time, macdsignal, label='MACD Signal')\nfor cross in crosses:\n    plt.plot(cross[0],cross[1],cross[2])\n#plt.plot(new_time, macdhist, label='MACD Histogram')\nplt.title(\"MACD Plot for BTC/USDT\")\nplt.xlabel(\"Open Time\")\nplt.ylabel(\"Value\")\nplt.legend()\nplt.show()\n```\n\nThis code for locating crossover points will certainly be improved in future posts, but this is a good start, and locating these points will make it very easy to backtest this trading strategy. \n\n \n# Looking Ahead \n\nIn the next installment in this series, we will be cleaning up the code to generate our indicators into more versatile functions and classes, adding support for more indicators, and begin creating our backtesting tools to analyze the performance of various strategies.\n\nLooking even further ahead, I'll be covering:\n* Making our bot run in realtime\n* Expanding backtesting and analytics to read and write data to a MySQL database\n* Many more trading strategies \n* More efficient methods for scanning multiple trading pairs\n* Using TensorFlow to utilize machine learning for tuning weight of indicators in deciding on potential trades\n* And much more\n\nAny requests for future topics to cover or general feedback are greatly appreciated.\n\nThanks for reading,\nAndrew",
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      "body": "Nice initiative ! I too am fond of trading bots...\nWhat's the trading strategy you will apply with this bot ? Will this journey will just be about coding a bot or will we have basktestings, discussion about strategies, etc. ?\nFollowing you anyway, and looking forward to see where this goes...",
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      "body": "Congratulations @genesiscrypto! You have completed some achievement on Steemit and have been rewarded with new badge(s) :\n\n[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/firstpost.png)](http://steemitboard.com/@genesiscrypto) You published your First Post\n[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/firstvote.png)](http://steemitboard.com/@genesiscrypto) You made your First Vote\n[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/firstvoted.png)](http://steemitboard.com/@genesiscrypto) You got a First Vote\n\nClick on any badge to view your own Board of Honor on SteemitBoard.\nFor more information about SteemitBoard, click [here](https://steemit.com/@steemitboard)\n\nIf you no longer want to receive notifications, reply to this comment with the word `STOP`\n\n> Upvote this notification to help all Steemit users. Learn why [here](https://steemit.com/steemitboard/@steemitboard/http-i-cubeupload-com-7ciqeo-png)!",
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      "body": "# Introduction\n\nIn this post, I'll be going through the basics of the tools that we'll be using to build our cryptocurrency trading bot.\n\n# Step One - Download PyCharm and Create Working Directory\n\nIf you already have a Python IDE that you prefer to work, then by all means, use what is most comfortable to you. For the sake of these tutorials I'll be using PyCharm, which can be downloaded [here](https://www.jetbrains.com/pycharm/download/#section=windows) (the community edition will work just fine). Once PyCharm is downloaded and opened up, create a new project anywhere you like. This will be where we store all python and text files needed to create the bot.\n\n# Step Two - Set up Binance Account and Get API Keys\n\nRegistering an account on Binance is easy, you simply click on this [link](https://www.binance.com/), and sign up.  Once you have an account, select the user icon shown below, and then press account.\n\n![clickhere.png](https://steemitimages.com/DQmf4VpxyBX1Wqum5sjh8tHEu3p6xtZvphshxPbxyvidBDu/clickhere.png)\n\nOnce on the account page, find the box labeled API, and click enable.\n\nNext, enter an API key label (this can be anything), click Create New Key.\n\nOnce you have your API Key and Secret Key, back them up in a secure location. What I did is create a file called credentials.txt in the working directory which we created in Step One, and pasted in the API Key on line one, and the secret key on line two. Later on in the post, I'll show you how to setup the Python-Binance API to use the keys stored in the credentials.txt file.\n\n# Step Three - Install the python-binance Client\n\nThe [python-binance client](https://github.com/sammchardy/python-binance) by sammchardy will be our way of communicating with Binance from within Python. This Python Binance API is the most widely used and most well documented. The library can downloaded with pip using the command: \n```\npip install python-binance\n```\nI ran into an issue on Windows 10 where the install would fail with exit code 1158, so if you run into this issue follow the steps in [this post](https://stackoverflow.com/questions/43858836/python-installing-clarifai-vs14-0-link-exe-failed-with-exit-status-1158/44563421) which should hopefully solve that issue.\n\nOnce the python-binance library is installed, we're ready to connect to our Binance account with Python.\n\n# Step Four - Connect to Binance\n\nTo connect to Binance we'll first need to create  a python file in our working directory, I named mine trader.py. In this file paste in the following code:\n\n```python3.6\nfrom binance.client import Client\n\nclass Trader:\n    def __init__(self, file):\n        self.connect(file)\n\n    \"\"\" Creates Binance client \"\"\"\n    def connect(self,file):\n        lines = [line.rstrip('\\n') for line in open(file)]\n        key = lines[0]\n        secret = lines[1]\n        self.client = Client(key, secret)\n\n    \"\"\" Gets all account balances \"\"\"\n    def getBalances(self):\n        prices = self.client.get_withdraw_history()\n        return prices\n\nfilename = 'credentials.txt'\ntrader = Trader(filename)\nbalances = trader.getBalances()\nprint(balances)\n```\n\nIf you run the following python script, you should get a print out of your account balances. If you get this result, then congratulations, you've successfully connected to Binance via Python!\n\nIf you don't get the following result, there are a number of issues that you may have had. Some things to check for: \n* Ensure that Python 3.6 (or newer) is installed on your system\n* If the binance.client library is not found, retry the previous pip install and fix any errors that may arise until the library successfully downloads\n* If you receive  an error relating to a Timestamp request error, check [this page](https://github.com/ccxt/ccxt/issues/850) to figure out how to sync your system clock to match Binance\n\nIf you are having problems with this setup that you can't seem to fix, leave a comment on this post, and I'll try my best to help you resolve any issues.\n\nAt this point you should have complete access to the Binance API, I encourage you to check out the python-binance [documentation](https://python-binance.readthedocs.io/en/latest/) and mess around with the commands that are available, because these commands will be the basis for making trades moving forward.\n\nIn the next post, we'll go over how to setup TA-LIB and begin generating some indicators based on historical price data.\n\nThanks for reading,\nAndrew",
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      "body": "✅  @genesiscrypto, congratulations on making your first post! **I gave you an upvote!**<br><br>**Please give me a follow** and take a moment to read [this post](https://steemit.com/spam/@pleasestop/introducing-pleasestop-here-to-reduce-comment-spam) regarding commenting and spam.<br>(tl;dr - if you spam, you will be flagged!)",
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