@run-out
Read "Cheat-on-open" again.
You can buy shares with next day open price.

@hghhgghdf-dfdf
That's genius idea. I wonder why there is no such method provided by backtrader. Just wonder. Anyway, thank you so much.

What I want to do is that in tick chart,
price : buy at market price(maybe next tick price),
size : all avaliable size(volume) in that tick, all-in my account money

For example,
I have money 10000, and then tick-chart looks like below.
(tick-chart)
9:5:5 volume 12, price 60
9:5:5 volume 100, price 55
9:5:6 volume 80, price 60
9:5:7 volume 67, price 65

then,
I expect to buy size 12(price 60) + size 100(price 55) + size 63(price 60). (because I dont have enough money at last)

But it is so hard to make such code with backtrader.

Is there any easy way of doing it?

I am beginner of Backtrader. And I am reading all the document but it is too complicated for me.
I really appreciate for your help.

Below is code sample:
(I got order margin here.)

def __init__(self):
    
    self.order = None
    self.buytime = None
    self.dataclose = self.datas[0].close
    self.dataopen = self.datas[0].open
    self.minvolume = self.datas[1].volume
    self.market_volume = self.datas[0].volume
    self.time = self.datas[0].datetime
    self.mintime = self.datas[1].datetime
    self.buy_continue = False
    self.volume_sma = bt.indicators.SimpleMovingAverage(self.datas[1].volume, period=10)

def log(self, txt, dt=None):
    print('%s, %s' % (self.time.datetime(), txt))

def notify_order(self, order):
    if order.status in [order.Submitted, order.Accepted]:
        return

    if order.status in [order.Completed]:
        if order.isbuy():
            self.log('BUY EXECUTED, {} {}'.format(order.executed.price, order.executed.size))
        elif order.issell():
            self.log('SELL EXECUTED, {} {}'.format(order.executed.price, order.executed.size))

    elif order.status in [order.Canceled, order.Margin, order.Rejected]:
        self.log('{} {} Order Canceled/Margin/Rejected'.format(order.created.price, order.created.size))

    if not order.alive:
        self.order = None


def next(self):

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

        if self.buy_continue:
            if self.broker.get_cash() > 0:
                self.order = self.buy(size=self.market_volume[0])

        else:
            if not self.position:
                if self.time.time() > datetime.time(9, 5, 0) and self.time.time() < datetime.time(14, 00, 00):
                    if self.volume_sma[0] * 0.5 <= self.minvolume[0]:
                        self.buy_continue = True
                        if self.broker.get_cash() > 0:
                            size = int(self.broker.get_cash() / self.dataclose[0])
                            self.order = self.buy(size=size)

if name == 'main':

cerebro = bt.Cerebro()

modpath = os.path.dirname(os.path.dirname(os.path.abspath(sys.argv[0])))
datapath = os.path.join(modpath, 'database/000250.csv')

dataframe = pd.read_csv(datapath, parse_dates=True, index_col=0, 
                            dtype={"Close":'float', "Volume":'int', "Open":'float', 'High':'float', 'Low':'float'})
dataframe["Datetime"] = pd.to_datetime(dataframe["Datetime"])   

cerebro.addstrategy(FatherStrategy)


data = bt.feeds.PandasData(dataname=dataframe)
cerebro.adddata(data)
data = cerebro.resampledata(data, timeframe=bt.TimeFrame.Minutes)

cerebro.broker.setcash(10000000.0)
cerebro.broker.setcommission(commission=0.0175)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

cerebro.run()

print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

cerebro.plot(style='bar')