Backtrader 튜토리얼 | 데이터접근, 전략설정, 매수, 매도, 수수료설정

Backtrader는 투자전략을 세우고 과거데이터들을 통해 투자전략을 검증하기 위해 사용되는 파이썬 프레임워크 입니다.

Backtrader 튜토리얼 | 설치, 기본설정, 금액설정, 데이터입력

 

Backtrader 데이터접근

사용할 데이터 파일

orcl-1995-2014.txt

0.33MB

 

 

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt


# Create a Stratey
class TestStrategy(bt.Strategy):

    def log(self, txt, dt=None):
        ''' Logging function for this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close, %.2f' % self.dataclose[0])


if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, 'orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values before this date
        todate=datetime.datetime(2000, 12, 31),
        # Do not pass values after this date
        reverse=False)

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

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

 

아래 함수를 통해 전략을 설정할 수 있습니다.

cerebro.addstrategy(전략Class)

 

전략을 수정하기 위해서는 class를 인자로 넘겨주어야 합니다. 이때 Class는 bt.Strategy를 상속받아 생성합니다.

# Create a Stratey
class TestStrategy(bt.Strategy):

    def log(self, txt, dt=None):
        ''' Logging function for this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close, %.2f' % self.dataclose[0])

 위와 같이 받은  cerebro.adddata(data)에서 받은 data를 활용할 수 있습니다. 종가 데이터를 추출하여 날짜와 종가데이터를 출력하는 방식입니다.

 

 

전략설정(매수)

3일연속 하락한 경우 구매하는 전략을 만들어 봅시다.

# Create a Stratey
class TestStrategy(bt.Strategy):

    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close, %.2f' % self.dataclose[0])

        if self.dataclose[0] < self.dataclose[-1]:
            # current close less than previous close

            if self.dataclose[-1] < self.dataclose[-2]:
                # previous close less than the previous close

                # BUY, BUY, BUY!!! (with all possible default parameters)
                self.log('BUY CREATE, %.2f' % self.dataclose[0])
                self.buy()

 

buy()는 구매함수입니다. 현재종가 < 1일전종가 < 2일전종가 일 경우 buy()함수가 실행됩니다.

 

 

전략설정(매도)

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt


# Create a Stratey
class TestStrategy(bt.Strategy):

    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

        # To keep track of pending orders
        self.order = None

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # Buy/Sell order submitted/accepted to/by broker - Nothing to do
            return

        # Check if an order has been completed
        # Attention: broker could reject order if not enough cash
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log('BUY EXECUTED, %.2f' % order.executed.price)
            elif order.issell():
                self.log('SELL EXECUTED, %.2f' % order.executed.price)

            self.bar_executed = len(self)

        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Canceled/Margin/Rejected')

        # Write down: no pending order
        self.order = None

    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close, %.2f' % self.dataclose[0])

        # Check if an order is pending ... if yes, we cannot send a 2nd one
        if self.order:
            return

        # Check if we are in the market
        if not self.position:

            # Not yet ... we MIGHT BUY if ...
            if self.dataclose[0] < self.dataclose[-1]:
                    # current close less than previous close

                    if self.dataclose[-1] < self.dataclose[-2]:
                        # previous close less than the previous close

                        # BUY, BUY, BUY!!! (with default parameters)
                        self.log('BUY CREATE, %.2f' % self.dataclose[0])

                        # Keep track of the created order to avoid a 2nd order
                        self.order = self.buy()

        else:

            # Already in the market ... we might sell
            if len(self) >= (self.bar_executed + 5):
                # SELL, SELL, SELL!!! (with all possible default parameters)
                self.log('SELL CREATE, %.2f' % self.dataclose[0])

                # Keep track of the created order to avoid a 2nd order
                self.order = self.sell()

 

notify_order(self, order) 함수는 주문상태에 따라 내용을 출력해주는 함수입니다. 위 경우 주문완료 되거나 주문이 거절된 경우 로그를 통해 알려줍니다.

 

self.order를 통해 매매대기중인 경우 매매를 실행하지 않습니다.

self.position을 통해 포지션을 확인하고 포지션이 없다면 아래 조건에 따라 매수를 진행하며, 이미 주식을 보유하고 있다면 조건에 맞게 매도합니다.

 

판매전략도 추가되었습니다. 매수 후 5일 뒤 판매하는 전략입니다.

sell() 판매함수 입니다.

 

 

매매비용 설정

주식매매에는 매매비용도 고려하여야 합니다.

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # Buy/Sell order submitted/accepted to/by broker - Nothing to do
            return

        # Check if an order has been completed
        # Attention: broker could reject order if not enough cash
        if 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))

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

            self.bar_executed = len(self)

        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Canceled/Margin/Rejected')

        self.order = None

    def notify_trade(self, trade):
        if not trade.isclosed:
            return

        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
                 (trade.pnl, trade.pnlcomm))

   
						""


    # Set the commission - 0.1% ... divide by 100 to remove the %
    cerebro.broker.setcommission(commission=0.001)


						""

 

cerebri,broker.setcommission(수수료)함수는 수수료를 결정합니다. 위의 경우 1/1000으로 매매수수료를 결정하였습니다.

order.executed.price 매도가격

order.executed.value 매수가격

order.executed.comm 매매비용

 

notify_trade(self, trade)함수의 경우 거래가 완료될 시 수익률과 수수료를 출력해 줍니다.

 

 

참고사이트

Quickstart Guide - Backtrader

 

 

다음글

Backtrader 튜토리얼 | 매개변수, 매매 단위설정, 다양한 지표추가, 매매전략 최적화