Outputs -
ref - bt's unique trade identifier
ticker - data feed name
datein - date and time of trade opening
pricein - price of trade entry
dir - long or short
dateout - date and time of trade closing
priceout - price of trade exit
chng% - exit price to entry price ratio
pnl - money profit/loss per trade
pnl% - proft/loss in %s to broker's value at the trade closing
cumpnl - cumulative profit/loss
size - max position size during trade
value - max trade value
nbars - trade duration in bars
pnl/bar - profit/loss per bar
mfe% - max favorable excursion
mae% - max adverse excursion

Hi all

I've used Amibroker for some time and liked their trade list output. So written the analyzer for closed trades which provides similar information:

  chng%    pnl%    mae%    pricein  ticker      pnl/bar    size     value    mfe%    priceout       pnl    cumpnl  dateout       ref    nbars  dir    datein
-------  ------  ------  ---------  --------  ---------  ------  --------  ------  ----------  --------  --------  ----------  -----  -------  -----  ----------
  -6.07   -2.82  -10.12    281.397  SPY         -271.68     159   44742.1    0.68      264.31  -2716.78  -2716.78  2018-02-14      1       10  long   2018-01-31
  -1.59   -0.71   -2.2     119.128  TLT          -68.55     363   43243.6    1.01      117.24   -685.47  -3402.25  2018-02-22      2       10  long   2018-02-07
   1.21   -0.54   -4.01    268.163  SPY          -51.3     -158  -42369.7    1.44      271.41   -513.04  -3915.29  2018-03-01      3       10  short  2018-02-14
   1.89   -0.84   -2.16    117.946  TLT          -53.82    -363  -42814.4    0.8       120.17   -807.27  -4722.56  2018-03-15      4       15  short  2018-02-22
  -1.98   -1.26   -6       270.915  SPY          -49.35     158   42804.6    3.5       265.55  -1184.35  -5906.91  2018-04-05      5       24  long   2018-03-01
  -1.57    0.71   -0.41    265.55   SPY          336.49    -161  -42753.6    2.84      261.37    672.98  -5233.93  2018-04-09      7        2  short  2018-04-05
   0.93    0.42   -1.09    119.413  TLT           19.87     359   42869.2    2.47      120.52    397.45  -4836.48  2018-04-13      6       20  long   2018-03-15
  -0.89    0.4    -1.35    119.962  TLT           77.43    -361  -43306.4    1.28      118.89    387.13  -4449.35  2018-04-20      9        5  short  2018-04-13
   2.9     1.76   -2.06    264.486  SPY           71.27     165   43126.1    3.28      272.16   1710.47  -2738.88  2018-05-11      8       24  long   2018-04-09
  -0.16    0.07   -0.61    272.585  SPY            6.96    -160  -43613.6    0.94      272.15     69.6   -2669.28  2018-05-25     11       10  short  2018-05-11
   0.95    0.32   -2.43    118.985  TLT           10.37     365   43429.6    2.97      120.11    310.98  -2358.3   2018-06-04     10       30  long   2018-04-20
  -1.18    0.26   -0.32    120.11   TLT           86.15    -182  -21860      1.41      118.69    258.44  -2099.86  2018-06-07     13        3  short  2018-06-04
   0.2     0.09   -0.01    118.69   TLT           29.76     372   44152.7    1.52      118.93     89.28  -2010.58  2018-06-12     14        3  long   2018-06-07
   2.36   -1.08   -3       118.93   TLT          -74.67    -372  -44242      0.25      121.74  -1045.32  -3055.9   2018-07-02     15       14  short  2018-06-12
  -0.78   -0.33   -2.73    275.275  SPY          -11.28     159   43768.8    1.53      273.14   -315.8   -3371.7   2018-07-06     12       28  long   2018-05-25
   1.88   -0.85   -2.3     273.14   SPY         -204.31    -159  -43429.3    0.16      278.28   -817.26  -4188.96  2018-07-12     17        4  short  2018-07-06
  -2.24   -1.01   -2.35    122.013  TLT          -64.31     353   43070.5    0.74      119.28   -964.63  -5153.59  2018-07-24     16       15  long   2018-07-02
   2.55    0.86   -0.24    278.28   SPY           45.82     156   43411.7    2.78      285.39    824.76  -4328.83  2018-08-07     18       18  long   2018-07-12
  -0.02    0.01   -1.01    119.053  TLT            0.81    -362  -43097      0.83      119.03      8.15  -4320.68  2018-08-07     19       10  short  2018-07-24

Feel free to use/test it, let me know about any bugs found. The script:

# Trade list similar to Amibroker output
class trade_list(bt.Analyzer):

    def get_analysis(self):

        return self.trades


    def __init__(self):

        self.trades = []
        self.cumprofit = 0.0


    def notify_trade(self, trade):

        if trade.isclosed:

            brokervalue = self.strategy.broker.getvalue()

            dir = 'short'
            if trade.history[0].event.size > 0: dir = 'long'

            pricein = trade.history[len(trade.history)-1].status.price
            priceout = trade.history[len(trade.history)-1].event.price
            datein = bt.num2date(trade.history[0].status.dt)
            dateout = bt.num2date(trade.history[len(trade.history)-1].status.dt)
            if trade.data._timeframe >= bt.TimeFrame.Days:
                datein = datein.date()
                dateout = dateout.date()

            pcntchange = 100 * priceout / pricein - 100
            pnl = trade.history[len(trade.history)-1].status.pnlcomm
            pnlpcnt = 100 * pnl / brokervalue
            barlen = trade.history[len(trade.history)-1].status.barlen
            pbar = pnl / barlen
            self.cumprofit += pnl

            size = value = 0.0
            for record in trade.history:
                if abs(size) < abs(record.status.size):
                    size = record.status.size
                    value = record.status.value

            highest_in_trade = max(trade.data.high.get(ago=0, size=barlen+1))
            lowest_in_trade = min(trade.data.low.get(ago=0, size=barlen+1))
            hp = 100 * (highest_in_trade - pricein) / pricein
            lp = 100 * (lowest_in_trade - pricein) / pricein
            if dir == 'long':
                mfe = hp
                mae = lp
            if dir == 'short':
                mfe = -lp
                mae = -hp

            self.trades.append({'ref': trade.ref, 'ticker': trade.data._name, 'dir': dir,
                 'datein': datein, 'pricein': pricein, 'dateout': dateout, 'priceout': priceout,
                 'chng%': round(pcntchange, 2), 'pnl': pnl, 'pnl%': round(pnlpcnt, 2),
                 'size': size, 'value': value, 'cumpnl': self.cumprofit,
                 'nbars': barlen, 'pnl/bar': round(pbar, 2),
                 'mfe%': round(mfe, 2), 'mae%': round(mae, 2)})

It returns the list of dictionaries. In order to print it as a table above I've used tabulate module:

from tabulate import tabulate
...
    # add analyzers
    cerebro.addanalyzer(trade_list, _name='trade_list')
...
    # get analyzers data
    maemfe = strats[0].analyzers.trade_list.get_analysis()
    print (tabulate(maemfe, headers="keys"))

Drawdown analyzer works with the historical broker value curve. You calculate your results based in closed trades equity, no in-trade drawdowns are considered. The results should be different in most of the cases.

Looks like your script makes trades based on random numbers. So I wouldn't be surprised if it will give different stats with every new run.

Show the error message.

@bb2 I might be wrong, but looks like your baseline data obtained from ib is resampled to 30 sec bars during data delivery. So there are no tick data available in the system.

Your approach will ruin indicators which use full OHLC values and limit/stop order execution.

If you want orders to be executed on close than probably better to use cheat-on-close and market orders and original OHLC data.

So depends on your goals.

As an idea (didn't test it):

class MyStochastic(bt.Indicator):
    lines = ('MyStoch', 'Stoc', 'S1',)
    params = (('period', 9),)

    def __init__(self):
   
        self.f = math.degrees(1.44 * 3.14159 / 10)
        self.a1 = math.exp(-self.f)
        self.b1 = 2 * self.a1 * math.cos(self.f)
        self.c2 = self.b1
        self.c3 = -self.a1 * self.a1
        self.c1 = 1 - self.c2 - self.c3

	self.hhigh = bt.Highest(period = self.p.period)
	self.llow = bt.Lowest(period = self.p.period)
        
	self.l.Stoc = (self.datas[0].close - self.hhigh) / (self.hhigh - self.llow)
        self.l.S1 = self.Stoc(-1)
        self.l.MyStoch = self.c1 * (self.Stoc + self.S1) / 2
       
    def next(self):
		
        self.l.MyStoch[0] = self.l.MyStoch[0] + self.c2 * self.l.MyStoch[-1] +  self.c3 * self.l.MyStoch[-2]

By default it will be executed on the next available price, which is next bar open. Same as other orders.

@guwop check the slicing approaches in Concepts section. With .get and max() operator you might have better coding in next().

Honestly I think that loop doesn't work as you want.

Also backtrader has built-in indicators to define highest high and lowest low data values for certain period.