@dmitry-prokopiev said in How to use EMA without a strategy:

it's impossible, use ta-lib ...

Thank you

Hi

I want to use backtrader EMA without a strategy to calculate EMA for an array of close values, but I don't know how the data should look like in order for backtrader to accept it

from backtrader.indicators import EMA

random_array = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

value = EMA(random_array, period=10)[0]

print(value)


#output: IndexError: array index out of range

@dasch Thanks man, but for seek of simplicity I will stick with the first approach for now since it's working and I already have the data of time frames that I need, but now I'm trying to use the first approach in the live trading, so let me know if you can help with that but without using the resample data, I want to achieve that with first approach

@run-out I have done that and in the first approach the data was correct,
in next method I print the datetime along with other values OHLVC, and the values were matching perfectly with the data
source in the first approach, however in the second approach it was completely different values! but I still don't know where is the problem in approach 2!

@aqa20 I'm sorry actually It don't get different result when adding, 5 and 60 compression permeates.

so the answer for this question:
" [Do you have a different result in approach 1 if you add, along with the timeframe parameter, the parameters compression = 5 & compression = 60 to data & data1 ?](link url)"
Is no

@emr
The differences is in the chart and the time execution orders,
In approach one if the buy order was executed on [05-03-21 12:00], the buy order will be executed on [05-03-21 13:00] on the the second approach, and same for sell orders.

Do you know if this compression is the same as when done by backtrader (ie bar2edge, boundoff, rightedge etc?

I believe so here is a sample of the 1m data

2021-03-24 00:00:00,54342.8,54359.0,54230.0,54260.35,1616544059999,2153
2021-03-24 00:01:00,54260.36,54422.43,54248.53,54408.13,1616544119999,1364
2021-03-24 00:02:00,54402.67,54515.73,54394.46,54515.73,1616544179999,1194
2021-03-24 00:03:00,54511.02,54606.15,54511.02,54592.13,1616544239999,1324
2021-03-24 00:04:00,54592.12,54669.35,54563.21,54597.62,1616544299999,1381
2021-03-24 00:05:00,54595.26,54626.39,54518.44,54555.23,1616544359999,909

Sample from the 1h data:

2021-03-24 00:00:00,255.5191,256.9375,250.8104,251.6466,1616547599999,51155
2021-03-24 01:00:00,251.5354,256.0,251.1514,255.6516,1616551199999,20791
2021-03-24 02:00:00,255.6426,256.9996,254.8947,255.6458,1616554799999,19972
2021-03-24 03:00:00,255.6458,256.6457,254.2957,256.1307,1616558399999,21009
2021-03-24 04:00:00,256.1242,256.98,253.9556,255.0727,1616561999999,22185
2021-03-24 05:00:00,255.0662,255.7192,252.8269,255.5432,1616565599999,18944

Sample from 5m data:

2021-03-24 00:00:00,255.5191,256.9375,254.6,256.813,1616544299999,3381
2021-03-24 00:05:00,256.8,256.9372,254.11,254.45,1616544599999,4276
2021-03-24 00:10:00,254.3532,255.0489,253.05,253.6577,1616544899999,4797
2021-03-24 00:15:00,253.8025,253.809,252.102,252.1554,1616545199999,4867
2021-03-24 00:20:00,252.305,253.4392,251.7721,252.3381,1616545499999,6368
2021-03-24 00:25:00,252.3289,252.63,250.8104,252.0226,1616545799999,6691
2021-03-24 00:30:00,252.0087,253.0,251.5001,252.4069,1616546099999,4125

Do you have a different result in approach 1 if you add, along with the timeframe parameter, the parameters compression = 5 & compression = 60 to data & data1 ?

Yes!

Have you checked for missing minutes in the 1 minute data file ?
Yes i checked for that using pandas

@dasch I've tried to add compression to the first approach, but I still get different result from 2 approaches

@dasch Well that's what I've done already, check the code

@dasch said in Different results with resample data and add data methods!:

our approach one you did not define the data compression, so all your csv are defined as 1 minute data, which is wrong. Try approach one with adding the param compression.

I'm loading data from 2 different files, the first one is 5m data, and the second is 1h data, and I haven't added the compression parameter in the first approach because the default value for compression is one

I get very different results between the two approaches shouldn't both of them deliver the same result!?

Approach one:

import backtrader as bt
from datetime import datetime


class CustomDataset(bt.feeds.GenericCSVData):
    ''' Instinitate CustomDataset '''
    params = (
        ('time', -1),
        ('datetime', 0),
        ('open', 1),
        ('high', 2),
        ('low', 3),
        ('close', 4),
        ('volume', 5),
        ('openinterest', 6),
    )


cerebro = bt.Cerebro()

data = CustomDataset(
    name='data',
    dataname='./5m-timeframe.csv',
    timeframe=bt.TimeFrame.Minutes,
    fromdate=datetime(2021, 3, 1),
    todate=datetime(2021, 3, 10)
)


data1 = CustomDataset(
    name='data1',
    dataname='./1h-timeframe.csv',
    timeframe=bt.TimeFrame.Minutes,
    fromdate=datetime(2021, 3, 1),
    todate=datetime(2021, 3, 10)
)

cerebro.adddata(data)
cerebro.adddata(data1)

Approach 2

import backtrader as bt
from datetime import datetime


class CustomDataset(bt.feeds.GenericCSVData):
    ''' Instinitate CustomDataset '''
    params = (
        ('time', -1),
        ('datetime', 0),
        ('open', 1),
        ('high', 2),
        ('low', 3),
        ('close', 4),
        ('volume', 5),
        ('openinterest', 6),
    )


cerebro = bt.Cerebro()

data = CustomDataset(
    name='data',
    dataname='./1m-timeframe.csv',
    timeframe=bt.TimeFrame.Minutes,
    fromdate=datetime(2021, 3, 1),
    todate=datetime(2021, 3, 10)
)

cerebro.resampledata(data, timeframe=bt.TimeFrame.Minutes, compression=5, name='5m')
cerebro.resampledata(data, timeframe=bt.TimeFrame.Minutes, compression=60, name='60m')