Optomisation giving different results

j45p41

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!

backtrader

@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

j45p41

Thank you that seemed to have worked!