@MuSaCN I think you're looking for that https://community.backtrader.com/topic/154/reach-_trades-list/4

Hi @kian-hong-Tan ! Welcome to the community !

• Actually I have generated an output with the class CashMarket like this:

stock = cerebro.addanalyzer(btanalyzers.CashMarket, _name='cashmarket')

then inject it in QuantStats:

qs.reports.html(stock, "SPY")

But I'm stuck with an error AttributeError: 'dict' object has no attribute 'min'
For info I have tried with dataset from a csv and data from panda file. Also I resample 1m bars to 4hours bars.
if @run-out you can help us :)
I suspect there is a min property to add to your CashMarket analyzer but not sure ...

• Thanks for the link but I really prefer Quanstats one :)

Thanks for your feedback @ab_trader !

Actually I had a deeper understanding of sizers with this article: https://www.backtrader.com/blog/posts/2019-08-29-fractional-sizes/fractional-sizes/

I thought that the size parameter was in percents ... but it has been much clearer with this piece of code:

def notify_order(self, order):
        if order.alive():
            return

        otypetxt = 'Buy ' if order.isbuy() else 'Sell'
        if order.status == order.Completed:
            self.loginfo(
                ('{} Order Completed - '
                 'Size: {} @Price: {} '
                 'Value: {:.2f} Comm: {:.2f}'),
                otypetxt, order.executed.size, order.executed.price,
                order.executed.value, order.executed.comm
            )
        else:
            self.loginfo('{} Order rejected', otypetxt)

As you said, logs saved me on this topic !

I came with this solution, and it's working like a charm:

o1 = self.buy()
self.sell(exectype=bt.Order.Limit, price=self.data.close * 1.03, size= o1.size * 0.25)
self.sell(exectype=bt.Order.Limit, price=self.data.close * 1.06, size=o1.size * 0.25)
self.sell(exectype=bt.Order.Limit, price=self.data.close * 1.12, size=o1.size * 0.25)
self.sell(exectype=bt.Order.Limit, price=self.data.close * 1.20, size=o1.size * 0.25)

On each TP, I sell a fragment (25%) of my position (+3%, +6%, +12% and +20%)

@kian-hong-Tan can you print the output of this line in your code:

stock = cerebro.addanalyzer(btanalyzers.CashMarket, _name='cashmarket')

I get something like this:

{
  datetime.datetime(2020, 4, 29, 0, 0): (100000.0, 100000.0),
  datetime.datetime(2020, 4, 29, 4, 0): (100000.0, 100000.0),
  ...
  datetime.datetime(2020, 8, 1, 0, 0): (811.8994327605933, 110828.6750226959)
}

Hello @Dimasik007 ! Maybe you should use the valid parameter for creating your orders (https://www.backtrader.com/docu/order/#order-creation)

for example when the buy order is filled, on notify_trade function you can create a sell order with a valid in datetime (+ 2/3 minutes)

Thanks @run-out for your snippet !
Thanks @eprice122 for your feedback, I was trying to debug it without any success !
I am gonna have a deeper look at those functions

Ok so it sounds that for yfinance api:

• intraday data cannot extend last 60 days
• valid intervals are: 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo

As I want to work with many timeframes (1h, 2h, 4h, ...) on different periods (weeks, months, years), I will probably have to deal with another strategy than using yahoo finance (probably still keeping SPY for benchmark, so I will just have to download SPY performance on different timeframes on csv files) ... What are your thoughts ?

Also what does your benchmark look alike @eprice122 ?

I have stats displayed in the terminal with your piece of code @kian-hong-Tan.
The charts are rendered in a lot of different plots, I will investigate how to deal with that

Another update, I have fixed the issue about the size:

I added this to my strategy:

def notify_cashvalue(self, cash, value):
      if (not self.position):
        self.Totalfragment = math.floor(cash / self.data.close[0])

Then in my next funtion I set 4 sizes:

OrderSize1 = self.Totalfragment * 0.25
OrderSize2 = self.Totalfragment * 0.25
OrderSize3 = self.Totalfragment * 0.25
OrderSize4 = self.Totalfragment - f1 - f2 - f3

and I set:

• brackets order 1 size with OrderSize1
• brackets order 2 size with OrderSize2
• brackets order 3 size with OrderSize3
• brackets order 4 size with OrderSize4

And everything work.

I don't think using notify_cashvalue for setting the order size is a good pattern ... Do you think there is a better implementation ?

• Using in strategy the function setsizer ?
• Using in sizer the function _getsizing ?

Thanks for your feebacks !

@Jakob-Schütte maybe self.data[0].close.get(size=self.p.period) instead of self.data0.close.get(size=self.p.period) ? 🙂

If I well understood, you probably need to use a mix between multiple data strategy and managing the size of the position in the strategy like I did here

It's both:

• You can set a limit order that will trigger as soon as the price hit the target price
• or you can set a simple buy order based on an SMA strategy like here that will trigger once the period is closed

@darkknight9394 @KK I tried to make it work here

html full report rendering is not working for the moment, I need to work on it ... if you find a solution in the mid time ... 😎

@KK here 🙂

@Jakob-Schütte maybe self.data[0].close.get(size=self.p.period) instead of self.data0.close.get(size=self.p.period) ? 🙂

@KK as far as I know pyfolio is not supported anymore with backtrader (there is a topic about quantstats as an alternative)

Hello @Dimasik007 ! Maybe you should use the valid parameter for creating your orders (https://www.backtrader.com/docu/order/#order-creation)

for example when the buy order is filled, on notify_trade function you can create a sell order with a valid in datetime (+ 2/3 minutes)

ok finally I went for a good old method:

print('--------------------')
# calculating bbw
bbw = (self.bbw.top - self.bbw.bot) / self.bbw.mid
# listing last 20 bbw
result_t = [self.calculateBBW(k) for k in range(-20, 0)]
# filtering nan value in the list
minbbw = 0 if math.isnan(min(result_t)) else min(result_t)
# comparing values
print(bbw <= minbbw)
print('--------------------')

I really struggle sometimes to understand the documentation when there are no examples

@ab_trader in the documentation it is explained that it calculates the lowest value for the data in a given period:

• lowest = min(data, period)

To me I assumed (and was mistaken as you said):

• that data is the bbw indicator
• period is the last 60 candles

So if I'm right, I should:

• create an array with the last 60 bbw values (bbwArray)
• find the minimum in the array
• compare this minmum with bbw[0]

bbw = (self.bbw.top - self.bbw.bot) / self.bbw.mid
minbbw = min(bbwArray, 60)
print(bbw == minbbw)

Right ?

I use the Lowest indicator.
I try to compare my bollinger bandwith value with the minimum one on a specific period (60).
Unfortunately the minimum one is always equal to my bollinger bandwith value calculated.

Any thoughts ?

class BaseStrategy(bt.Strategy):
    def __init__(self):
      self.bbw = bt.indicators.BollingerBandsPct()

    def next(self):
      print('--------------------')
      bbw = (self.bbw.top - self.bbw.bot) / self.bbw.mid
      minbbw = min(bbw, 60)
      print(bbw == minbbw) # alway True
      print('--------------------')