Now I got your idea! didn't realise there is such buy/sell observer.
now i know that the backtrader has a very flexilbe design and love it more.

Just a remark: can't the Sell/buy observer be a bit smarter so that it can always plot properly?

Thanks!

anyone could help?

it seems that it is because of the data in forex csv file: the number is too small,
it will work if i change the price to bigger values, such as from "000000;1.123210;1.123240;1.123200;1.123240;0"
to "000000;3210;3240;3200;3240;0".

so it looks a bug in backtrader when handling precision of the price.

@backtrader Hi sorry for the format issue, it's my first post and I have fixed it.
Could you have a look again?

Thanks!

@backtrader said in Sell/Buy signal icons are missing in the plot:

position as this

Wow, thanks for your quick reply!

The "buy/sell Icons" i meant is as following (in the black circle that i drew):

but they are missing in first picture (1 minutes csv data).
any idea?

Hello!
I am using the 1-minutes forex csv data for backtesting, odd thing observed is that the buy/sell icons are missing in the plotting.
using daily csv data from yahoo is fine, so looks there is some tricky issue in the csv part.
Could anybody please help me with the issue?

the code is

from datetime import datetime
import backtrader as bt

class SmaCross(bt.SignalStrategy):
    def __init__(self):
        sma1, sma2 = bt.ind.SMA(period=1), bt.ind.SMA(period=60)
        crossover = bt.ind.CrossOver(sma1, sma2)
        self.signal_add(bt.SIGNAL_LONG, crossover)


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

    # Add a strategy
    #cerebro.addstrategy(MyStrategy, period=15)

    cerebro.addstrategy(SmaCross)

    # sh: 000001.SS
    # BT: BTC-USD
    # SP500: ^GSPC
    #data0 = bt.feeds.YahooFinanceData(dataname='BTC-USD', fromdate=datetime(2018, 1, 1),
    #                                  todate=datetime(2019, 6, 1), decimals=5)

    data0 = bt.feeds.GenericCSVData(
    dataname='./eurusd-1m/DAT_ASCII_EURUSD_M1_201904.csv',

    fromdate=datetime(2019, 4, 1),
    todate=datetime(2019, 4, 10),

    nullvalue=0.0,

    dtformat=('%Y%m%d %H%M%S'),
    #tmformat=('%H:%M:%S'),

    datetime=0,
    time=-1,
    high=2,
    low=3,
    open=1,
    close=4,
    volume=-1,
    openinterest=-1,
    timeframe=bt.TimeFrame.Minutes,
    compression = 1,
    separator=';',
    decimals=5,
    headers=False
    )

    cerebro.resampledata(data0, timeframe=bt.TimeFrame.Minutes,
                          compression=30)
   
    # Set our desired cash start
    cerebro.broker.setcash(1000000.0)

    # Add a FixedSize sizer according to the stake
    #cerebro.addsizer(bt.sizers.FixedSize, stake=2)
    cerebro.addsizer(bt.sizers.PercentSizer, percents=90)

    # Set the commission - 0.1% ... divide by 100 to remove the %
    cerebro.broker.setcommission(commission=0.0005)

    # 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())
    cerebro.plot(style='bar')

The CSV data looks as following:

20190401 000000;1.123210;1.123240;1.123200;1.123240;0
20190401 000100;1.123230;1.123230;1.123140;1.123190;0
20190401 000200;1.123210;1.123310;1.123200;1.123310;0
20190401 000300;1.123330;1.123360;1.123310;1.123340;0
20190401 000400;1.123330;1.123330;1.123250;1.123250;0

The plotting: