explore_functions_strategy.py

import datetime
import os.path
import sys

import backtrader as bt
import backtrader.feeds as btfeed
import ccxt
import pandas as pd
from orderobserver import OrderObserver


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 firstStrategy(bt.Strategy):
    params = (
        ("period", 21),
        ("rsi_low", 41),
        ("rsi_high", 66),
    )

    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        dt = dt or self.data.datetime[0]
        if isinstance(dt, float):
            dt = bt.num2date(dt)
        print('%s, %s' % (dt.isoformat(), txt))

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # Buy/Sell order submitted/accepted to/by broker - Nothing to do
            self.log('ORDER ACCEPTED/SUBMITTED', dt=order.created.dt)
            self.order = order
            return

        if order.status in [order.Expired]:
            self.log('BUY EXPIRED')

        elif order.status in [order.Completed]:
            if order.isbuy():
                self.log(
                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                    (order.executed.price,
                     order.executed.value,
                     order.executed.comm))

            else:  # Sell
                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                         (order.executed.price,
                          order.executed.value,
                          order.executed.comm))

        # Sentinel to None: new orders allowed
        self.order = None

    def __init__(self):
        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)
        else:
            if self.rsi > self.params.rsi_high:
                self.sell(size=10)


# 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 STRATEGY
    cerebro.optstrategy(firstStrategy, period=range(10, 11), rsi_low=range(33, 34), rsi_high=range(60, 61))

    cerebro.addobserver(OrderObserver)

    # DATA FEED FROM EXCHANGE

    symbol = str('ETH/USDT')
    timeframe = str('15m')
    exchange = str('poloniex')
    exchange_out = str(exchange)
    start_date = str('2018-3-10 00:00:00')
    end_date = str('2018-3-16 19:00:00')
    get_data = False

    # 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 == True:
        hist_start_date = int(to_unix_time(start_date))
        hist_end_date = int(to_unix_time(end_date))
        data = exchange.fetch_ohlcv(symbol, timeframe, since=hist_start_date, limit=hist_end_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, )

    # Add the data to Cerebro
    cerebro.adddata(data)
    cerebro.addobserver(bt.observers.Trades)
    cerebro.addobserver(bt.observers.BuySell)

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

    # RUN STRATEGY THROUGH CEREBRO USING INPUT DATA
    opt_runs = cerebro.run()

    # 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

    print('Results: Ordered by Profit:')
    for result in by_PnL:
        print('Period: {}, rsi_low: {}, rsi_high: {}, PnL: {}'.format(result[0], result[1], result[2], result[3]))

    # cerebro.plot(style='candlestick')