3. Optomisation giving different results
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Optomisation giving different results

  • J

    Hi,

    first of all thank you all for all your hard work.

    I have gone from knowing very little python a few weeks ago to writing my first strategy in backtrader. I started with gekko but then said to myself that it was worth learning a platform with greater potential.

    I am however scratching my head at the moment. When I run my code and vary the optomisation parameters I get different results. E.g. when I find a set of optimal paramaters and then squeeze my ranges to near the optimal values something seems wrong.

    Here are some of my results:

    Period: 13, rsi_low: 32, rsi_high: 72, PnL: 2150.7
    cerebro.optstrategy(firstStrategy, period=range(10, 15), rsi_low=range(30, 35), rsi_high=range(55, 75))

    Period: 13, rsi_low: 32, rsi_high: 72, PnL: 2466.8
    cerebro.optstrategy(firstStrategy, period=range(13, 14), rsi_low=range(32, 33), rsi_high=range(72, 73))

    Period: 13, rsi_low: 32, rsi_high: 72, PnL: 2207.9
    cerebro.optstrategy(firstStrategy, period=range(12, 14), rsi_low=range(31, 33), rsi_high=range(71, 73))

    Here is my code:

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

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 __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=100)
        else:
            if self.rsi > self.params.rsi_high:
                self.sell(size=100)

# INPUT CONDITIONS TO FEED INTO CEREBRO IS ADDED HERE
if __name__ == '__main__':
    # Variable for our starting cash
    startcash = 1000000
    # Create an instance of cerebro
    cerebro = bt.Cerebro(optreturn=False)

    # Add our strategy
    cerebro.optstrategy(firstStrategy, period=range(12, 14), rsi_low=range(31, 33), rsi_high=range(71, 73))

    # DATA FEED FROM EXCHANGE

    symbol = str('ETH/USDT')
    timeframe = str('15m')
    exchange = str('poloniex')
    exchange_out = str(exchange)
    start_date = str('2018-3-16 00:00:00')
    end_date = str('2018-3-17 19:00:00')

    # 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

    # Get data
    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')
    #df = pd.DataFrame(data, columns=header).set_index('Timestamp')

    #Precision
    df = df.round(3)


    # Save it
    symbol_out = symbol.replace("/", "")
    filename = '{}-{}-{}.csv'.format(exchange_out, symbol_out, timeframe)
    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=60,)


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

    # 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]))

    Any ideas? Also if anybody reading this can tell me how to use the data frame without going via csv then that would also be great!

    0
    B
     1 Reply
  • B
    administrators

    @j45p41 said in Optomisation giving different results:

    data = exchange.fetch_ohlcv(symbol, timeframe, since=hist_start_date, limit=hist_end_date)

    Save it once to a file and use it always, rather than pulling data, because data can be back-adjusted.

    @j45p41 said in Optomisation giving different results:

    Also if anybody reading this can tell me how to use the data frame without going via csv then that would also be great!

    Use PandasData

    2
  • J

    Thank you that seemed to have worked!

    0
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