first_strategy_pickled.py

import datetime
import time
import os.path
import sys
import backtrader as bt
import backtrader.feeds as btfeed
import ccxt
import pandas as pd
import backtrader.analyzers as btanalyzers

from collections import OrderedDict


# DECLARE MODE FOR PROGRAM - OPTOMISATION OR STRATEGY
opt_mode = False

# CSV INPUT FILE FORMAT CONFIGURATION
class dataFeed(btfeed.GenericCSVData):
    params = (
        ('dtformat', '%Y-%m-%d %H:%M:%S'),
        ('datetime', 0),
        ('open', 1),
        ('high', 2),
        ('low', 3),
        ('close', 4),
        ('volume', 5),
        ('openinterest', -1)
    )
class OrderObserver(bt.observer.Observer):
    lines = ('created', 'expired',)

    plotinfo = dict(plot=True, subplot=True, plotlinelabels=True)

    plotlines = dict(
        created=dict(marker='*', markersize=8.0, color='lime', fillstyle='full'),
        expired=dict(marker='s', markersize=8.0, color='red', fillstyle='full')
    )

    def next(self):
        for order in self._owner._orderspending:
            if order.data is not self.data:
                continue

            if not order.isbuy():
                continue

            # Only interested in "buy" orders, because the sell orders
            # in the strategy are Market orders and will be immediately
            # executed

            if order.status in [bt.Order.Accepted, bt.Order.Submitted]:
                self.lines.created[0] = order.created.price

            elif order.status in [bt.Order.Expired]:
                self.lines.expired[0] = order.created.price

# MAIN STRATEGY DEFINITION - DEFINE VALUES HERE FOR NON-OPTOMISATION MODE
class firstStrategy(bt.Strategy):
    params = (
        ("period", 11),
        ("rsi_low", 45),
        ("rsi_high", 63),
    )

    def __init__(self):
        self.set_tradehistory(True) # SWITCHED THIS ON TO GET THE TRADE HISTORY
        self.startcash = self.broker.getvalue()
        self.rsi = bt.indicators.RSI_SMA(self.data.close, period=self.params.period)

    def next(self):
        if not self.position:
            if self.rsi < self.params.rsi_low:
                self.buy(size=10)
                #if opt_mode == False: print('{},BUY,{},PNL'.format(self.datetime.datetime(), self.data.close[0]))

        else:
            if self.rsi > self.params.rsi_high:
                self.sell(size=10)
                #if opt_mode == False: print('{},SELL,{},PNL'.format(self.datetime.datetime(), self.data.close[0]))

                tradesfordata = self._trades[data][0]  # list of trades for data

if opt_mode == False:
    def printTradeAnalysis(analyzer):
        '''
        Function to print the Technical Analysis results in a nice format.
        '''
        #Get the results we are interested in
        total_open = analyzer.total.open
        total_closed = analyzer.total.closed
        total_won = analyzer.won.total
        total_lost = analyzer.lost.total
        win_streak = analyzer.streak.won.longest
        lose_streak = analyzer.streak.lost.longest
        pnl_net = round(analyzer.pnl.net.total,2)
        strike_rate = round((total_won / total_closed) * 100,2)
        #Designate the rows
        h1 = ['Total Open', 'Total Closed', 'Total Won', 'Total Lost']
        h2 = ['Strike Rate','Win Streak', 'Losing Streak', 'PnL Net']
        r1 = [total_open, total_closed,total_won,total_lost]
        r2 = [strike_rate, win_streak, lose_streak, pnl_net]
        #Check which set of headers is the longest.
        if len(h1) > len(h2):
            header_length = len(h1)
        else:
            header_length = len(h2)
        #Print the rows
        print_list = [h1,r1,h2,r2]
        row_format ="{:<15}" * (header_length + 1)
        print("Trade Analysis Results:")
        for row in print_list:
            print(row_format.format('',*row))

    def printSQN(analyzer):
        sqn = round(analyzer.sqn,2)
        print('SQN: {}'.format(sqn))

    def notify_trade(self, trade):
        thedata = trade.data

# INPUT CONDITIONS TO FEED INTO CEREBRO IS ADDED HERE
if __name__ == '__main__':
    # Variable for our starting cash
    startcash = 10000
    # Create an instance of cerebro
    cerebro = bt.Cerebro(optreturn=False)

    # Add the analyzers we are interested in
    cerebro.addanalyzer(btanalyzers.Transactions, _name='mytransactions')
    cerebro.addanalyzer(btanalyzers.TradeAnalyzer, _name='mytrades')
    #cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="ta")
    #cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")

    # Timing the whole operation
    time_at_start = time.time()


    if opt_mode == True:
        # ADD STRATEGY OPTIMISATION
        cerebro.optstrategy(firstStrategy, period=range(11, 12), rsi_low=range(36, 37), rsi_high=range(61, 63))
    else:
        #ADD STRATEGY
        cerebro.addstrategy(firstStrategy)
        #ADD OBSERTVERS WIP
        cerebro.addobserver(bt.observers.Trades)
        #cerebro.addobserver(bt.observers.BuySell)
        #cerebro.addobserver(bt.observers.LogReturns)
        #cerebro.addobserver(bt.observers.LogReturns2)


    # DATA FEED FROM EXCHANGE
    symbol = str('ETH/USDT')
    timeframe = str('15m')
    exchange = str('poloniex')
    exchange_out = str(exchange)
    start_date = str('2017-1-1 00:00:00')
    get_data = True

    #So, let's say, you are fetching 2 days of 5m timeframe:
    #(1440 minutes in one day * 7 days) / 15 minutes = 576 candles

    num_of_candles = 672

    # Get our Exchange
    exchange = getattr(ccxt, exchange)()
    exchange.load_markets()


    def to_unix_time(timestamp):
        epoch = datetime.datetime.utcfromtimestamp(0)  # start of epoch time
        my_time = datetime.datetime.strptime(timestamp, "%Y-%m-%d %H:%M:%S")  # plugin your time object
        delta = my_time - epoch
        return delta.total_seconds() * 1000

    # CSV File Name
    symbol_out = symbol.replace("/", "")
    filename = '{}-{}-{}.csv'.format(exchange_out, symbol_out, timeframe)


    # Get data if needed

    if get_data == False:
        hist_start_date = int(to_unix_time(start_date))
        #data = exchange.fetch_ohlcv(symbol, timeframe, since=hist_start_date, limit=num_of_candles)
        data = exchange.fetch_ohlcv(symbol, timeframe, since=hist_start_date)
        header = ['Timestamp', 'Open', 'High', 'Low', 'Close', 'Volume']
        df = pd.DataFrame(data, columns=header)
        df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='ms')

        #Precision
        df = df.round(3)

        # Save it
        df.to_csv(filename, index= False)

    #READ DATA FROM CSV FILE
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath,str(filename))
    data = dataFeed(dataname=datapath, timeframe=bt.TimeFrame.Minutes, compression=15,
                    fromdate=datetime.datetime(2017, 1, 1),
                    todate=datetime.datetime(2017, 1, 31),)

    # Add the data to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(startcash)

    if opt_mode == False:
        # Add the analyzers we are interested in
        cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="ta")
        cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")

    # RUN STRATEGY THROUGH CEREBRO USING INPUT DATA
    # Timing the operation
    time_at_end = time.time()
    time_elapsed = round(time_at_end - time_at_start,2)
    print('Time elapsed: {} seconds'.format(time_elapsed))
    print ('Running Cerebro')
    opt_runs = cerebro.run()
    firstStrat = opt_runs[0]


    if opt_mode == True:
        # CREATE A LIST VARIABLE THAT CONTAINS RESULTS
        final_results_list = []
        for run in opt_runs:
            for strategy in run:
                value = round(strategy.broker.get_value(), 2)
                PnL = round(value - startcash, 2)
                period = strategy.params.period
                rsi_low = strategy.params.rsi_low
                rsi_high = strategy.params.rsi_high
                final_results_list.append([period, rsi_low, rsi_high, PnL])

        # Sort Results List
        by_period = sorted(final_results_list, key=lambda x: x[0])
        by_PnL = sorted(final_results_list, key=lambda x: x[3], reverse=True)

        # PRINT RESULTS IN OPTIMISATION AND FILTER TOP 3
        result_number = 0
        print('Results: Ordered by Profit:')
        for result in by_PnL:
            if result_number < 3:
                print('Period: {}, rsi_low: {}, rsi_high: {}, PnL: {}'.format(result[0], result[1], result[2], result[3]))
                result_number = result_number + 1

    # Timing the operation
    time_at_end = time.time()
    time_elapsed = round(time_at_end - time_at_start,2)
    trades = [str(trade).splitlines() for trade in list(firstStrat._trades.values())[0][0]]
    print(trades)

    print('Time elapsed: {} seconds'.format(time_elapsed))
    if opt_mode == False:
        # print the analyzers
        printTradeAnalysis(firstStrat.analyzers.ta.get_analysis())
        printSQN(firstStrat.analyzers.sqn.get_analysis())

        print('Transactions:', firstStrat.analyzers.mytransactions.get_analysis())
        print('TradeAnalyser:', firstStrat.analyzers.mytrades.get_analysis())



#Get final portfolio Value
        portvalue = cerebro.broker.getvalue()

        #Print out the final result
        print('Final Portfolio Value: ${}'.format(portvalue))

    cerebro.plot(style='candlestick')