Backtest multiple tickers at a same time

cwjeong

Hi, I'm a beginner using backtrader.

I have some data feeds (using sqlite from my local laptop), and I wanna backtest multiple tickers at a same time with a same logic.

My logic is simple. Buy stocks at a close price, Sell stocks at a open price next day.
I can run this logic when just backtest a stock.
But I have some trouble when adding tickers (two stocks)

My code is here.

class OverNight(bt.Strategy):

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

def __init__(self):
    self.inds = {}
    for i, d in enumerate(self.datas):
        self.inds[d] = dict()

    self.order = None           # Issued Order (not yet executed)
    self.buyprice = None
    self.buycomm = None


def next(self):
    for i, d in enumerate(self.datas):
        dt, dn = self.datetime.date(), d._name
        pos = self.getposition(d).size

        if not pos:
            self.buy(data=d, exectype=bt.Order.Close)
        else:
            self.close(data=d)
            self.buy(data=d, exectype=bt.Order.Close

def notify_order(self, order):
    if order.status != order.Completed:
        return

    if order.status in [order.Completed]:
        if order.isbuy():
            self.log('매수 완료 | 매수가격: %.2f, 총매매금액: %.2f, 수수료: %.2f' %
                     (order.executed.price, order.executed.value, order.executed.comm))

            self.buyprice = order.executed.price
            self.buycomm = order.executed.comm

        elif order.issell():
            self.log('매도 완료 | 매도가격: %.2f, 총매매금액: %.2f, 수수료: %.2f' %
                     (order.executed.price, order.executed.value, order.executed.comm))
            pass

        self.bar_executed = len(self)

    self.order = None

def notify_trade(self, trade):
    if trade.justopened:
        pass
    if not trade.isclosed:
        self.log('매매 손익 | 총손익: %.2f, 순손익: %.2f, 주식수: %.2f' % (trade.pnl, trade.pnlcomm, trade.size))
        self.log('총자산: %.2f' % self.broker.getvalue())

class maxRiskSizer(bt.Sizer):
'''
Returns the number of shares rounded down that can be purchased for the max risk tolerance
'''
params = (('risk', 0.03),)

def __init__(self):
    if self.p.risk > 1 or self.p.risk < 0:
        raise ValueError('The risk parameter is a percentage which must be'
                         'entered as a float. e.g. 0.5')

def _getsizing(self, comminfo, cash, data, isbuy):
    if isbuy == True:
        size = math.floor((self.broker.getvalue() * self.p.risk) / data[0])
        print('현금: %s, 주식수: %s, 주가: %s' % (math.floor(cash), size, data[0]))
    else:
        size = math.floor((self.broker.getvalue() * self.p.risk) / data[0]) * -1
        print('현금: %s, 주식수: %s, 주가: %s' % (math.floor(cash), size, data[0]))
    return size

if name == 'main':
cerebro = bt.Cerebro()

# 전략 추가
cerebro.addstrategy(OverNight)

# Analyzer
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="ta")
cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")

# 데이터 입력
data_path = 'E:/AutoTrading_test/DB/cmp_ohlcv.db'
sql = "SELECT * FROM '{0}' WHERE day > ""20201001"" ORDER BY day ASC"
con = sqlite3.connect(data_path)
code_list = pd.read_sql("SELECT * FROM cmp_master", con)

code_list = list(np.array(code_list['Code'].tolist()))
code_list = ['A005930', 'A000660']
for code in code_list:
    data = pd.read_sql(sql.format(code), con, index_col='day', parse_dates=['day'])
    data = bt.feeds.PandasData(dataname=data, name=code)

    cerebro.adddata(data)

startcash = 1000000
cerebro.broker.set_cash(startcash)
cerebro.broker.setcommission(0.0015)
cerebro.addsizer(maxRiskSizer, risk=0.7)

strategies = cerebro.run()
firstStrat = strategies[0]

portvalue = cerebro.broker.getvalue()

print('기초자산: %.2f' % startcash)
print('기말자산: %.2f' % portvalue)

# print the analyzers
printTradeAnalysis(firstStrat.analyzers.ta.get_analysis())
printSQN(firstStrat.analyzers.sqn.get_analysis())

cerebro.plot(style='candlestick')

Wayne Filkins 0

So are you saying you want to run the backtest on multiple stocks? Did you ever figure it out? I may have found something helpful a few weeks back, I will try to find it again.