only a guess.. you may need to add a warm up period to your indicator
class Dummy(Indicator):
lines = ('l',)
params = (('period', 16),)
def __init__(self):
self.addminperiod(self.params.period)
def next(self):
# Datetime of the latest completed bars in both Time-Frames
print(self.data0.datetime.datetime(0), end=": ")
print(self.data1.datetime.datetime(0))
When you pass close to RSI indicator.. its self.data.close not self.close
class RSI(bt.Strategy):
params = (('period', 14),)
def __init__(self):
self.rsi = bt.indicators.RSI_Safe(self.data0.close, period=14)
self.smooth_rsi = bt.indicators.ExpSmoothing(self.rsi, period=8)
BT is using adjusted close value to adjust other four values. Have a look at Data Feeds
Please check what are you printing. I did a quick test and it has time as well..
what about position size ??
12.76 x (7079.00 - 7468.99 ) - 71.50 - 67.77
@Ron-Aw said in Multi Asset position management:
I'm under the impression that if I want to go long on an asset with a market order, I should be using self.buy(price = self.datas[i].close[0], size = (My Size Here)). Is this the right way to open a buy order for a particular asset?
In Orders Doc there is a data parameter while sending the orders. So you can use that to send data specific orders.
@Ron-Aw said in Multi Asset position management:
Assume that I have 4 data feeds, if I want position details on the first asset, should I be using self.getposition(data = self.datas[0])?
Yes I think so. Position
@Ron-Aw said in Multi Asset position management:
The object returned by self.getposition(data = self.datas[i]) has 2 attributes namely size and price. If there are no open positions on the asset, self.getposition(data = self.datas[0]) should therefore evaluate to False right?
yes.. I normally use position.size it see long, short or no positions.
@Ron-Aw said in Multi Asset position management:
If I wish to close all positions, should I use a for loop to iterate over each data feeds and call self.close(self.datas[i]) on each iteration?
this is what I do.. But I dont know if there is an elegant way.
datas[0] this indexing is for the data that feed to BT.. if you load two data sets then you will have datas[0] and datas[1].
datas[0].close[0] is current close for data[0] and data[0].close[-1] is previous close.
You need to access the size attribute.
self.getposition(data=self.datas[0]).size > 0 for long
self.getposition(data=self.datas[0]).size < 0 for short
I never use fractional sizing, but I have seen people using it.
On the first look your csv index seems to be mixed up.. and Cerebro.run() is missing..
General note:
Please use ``` ticks to post the code rather than images.. Its hard to decode with images..
after a quick glance, you are setting open, high, low and close to be absent by setting them -1. Im not sure if you need a pandas class at all as BT has generic pandas support. For example
data_file = 'contracts_1_min.csv'
# Create a Data Feed and add to cerebro
df = pd.read_csv(data_file, header=0, index_col=0, parse_dates=True)
data0 = bt.feeds.PandasData(dataname=df,
timeframe=bt.TimeFrame.Minutes,
compression=1,
sessionstart=datetime.time(9, 15),
sessionend=datetime.time(15, 30),
openinterest=-1,
fromdate=datetime.datetime(2011,1, 3),
todate=datetime.datetime(2011, 11, 4)
)
only a guess.. you may need to add a warm up period to your indicator
class Dummy(Indicator):
lines = ('l',)
params = (('period', 16),)
def __init__(self):
self.addminperiod(self.params.period)
def next(self):
# Datetime of the latest completed bars in both Time-Frames
print(self.data0.datetime.datetime(0), end=": ")
print(self.data1.datetime.datetime(0))
@kicgahi said in logging timestamp produces an entirely different number:
dt = datetime.datetime.fromtimestamp(self.data.datetime[0])
your print log is converting the timestamp which is the issue here. Give it a try with following
def log(self, txt, dt=None):
''' Logging function fot this strategy'''
dt = dt or self.datas[0].datetime.date(0)
print('%s, %s' % (dt.isoformat(), txt))
@Impulse-Five said in Complex trading logic:
which is activated on the future value of an indicator
I don't understand this question. how can one predict the future value of a indicator ( if you mean Ichimoku otherwise )
@Impulse-Five said in Complex trading logic:
activate trailing stop-loss when indicator1 gives signal1?
Yes its possible. I normally keep my stop loss in my code and send order when its triggered.
@Impulse-Five said in Complex trading logic:
otherwise the trade should reach reach the dynamic stop-loss set at trade entry (multiple of Average True range) or exit with the breakeven after the price closes above entry (break-even activator signal - also activated after trade is placed - not at trade placement)
I trail my stop loss based on price movement , and I also have a threshold for activating it. but I keep my SL at system and not with broker.
Essentially BT can fit your requirements.
Please check what are you printing. I did a quick test and it has time as well..
Note: Please use backticks ``` to post your code, otherwise its hard to read.
Here is the example of how to use BT with multi data.
https://www.backtrader.com/blog/posts/2017-04-09-multi-example/multi-example/
You need to convert the unixtimestamp to date using bt.date2num function.. Have a look at the
Binary Feed Doc
You can use open time or close time depending on your choice.
Have a look here..
https://github.com/Dave-Vallance/bt-ccxt-store/blob/master/samples/kraken-example.py
There is a sample script for kraken.
Thanks for looking in to it. Very much appreciate it. I hope it was useful.