Using tick data and timeseries data together

[ct]

Hi,

I want to use simple strategy. I tried a lot ways but I can't.

Strategy must be use tick data (reading from csv), indicators must be use timeseries (resampled from tick) data

When latest price changed (on tick data):
if latest price (tick) >= indicator (ex: SMA(55)) latest bar close (based on timeseries (ex daily))
buy()
if latest price (tick) <= indicator (ex: SMA(20)) latest bar close (based on timeseries (ex daily))
sell()

Could you help me?

Best,

ct

class TestStrategy(bt.Strategy):
    
    def log(self, txt, dt=None):
        dt = dt or self.datas[0].datetime.datetime(0)
        print('%s, %s' % (dt, 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 enougth 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):
        up_value = <--- I need SMA(55) latest bar close
        down_value = <--- I need SMA(20) latest bar close
        self.log('Close, price: %.2f - up: %.2f' % (self.dataclose[0], up_value))

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

        if self.position.size == 0 : # FLAT
            if self.dataclose[0] > up_value:
                self.log('BUY CREATE, %.2f' % self.dataclose[0])
                self.order = self.buy()
            elif self.dataclose[0] < down_value:
                self.log('SELL CREATE, %.2f' % self.dataclose[0])
                self.order = self.sell()

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

    # Load the Data
    datapath = 'csv/df2_tick_reversed.csv'

    tick_data = btfeeds.GenericCSVData(
        dataname=datapath,
        timeframe=bt.TimeFrame.Ticks
    )
    
    # cerebro.addindicator(TURTLE, period=10)
    # period_data = cerebro.resampledata(dataname=tick_data, timeframe=bt.TimeFrame.Days)

    # Add the Data Feed to Cerebro
    # cerebro.adddata(tick_data)
    cerebro.resampledata(dataname=tick_data, timeframe=bt.TimeFrame.Days)
    # cerebro.adddata(period_data)

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)
    cerebro.broker.setcommission(commission=0.001) # 0.1% ... divide by 100 to remove the %

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

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

    # Run over everything
    cerebro.run()

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

[backtrader]

To simulate a raw tick data one can assign the value of the tick to the fields of the OHLC. See

• Community - backtest with tick data, get period datas

With that in mind the value of close (for example) can be used as the tick value (And has to be added with addata)

The 2nd stream, the resampled one, is created, as already present in the code, by issuing resampledata.

The tick data would be in data0 and the resampled in data1