@run-out said in What are different between bta-lib, ta-lib and internal BackTrader indicators?:

The last library is the very newly minted bta-lib. This was created by the maker of backtrader as a handy library for doing technical analysis when using pandas dataframes. In a nutshell, you can put in a OHLCV dataframe and BTA-LIB will spit out the indicator. It works quite well and is easier to use that the original ta-lib library.

Does bta-lib interact with the backtrader framework? Or is it meant to be used seprately?

I can't seem to get my conditional if statement to work!

It works properly with ONE condition (both seem to work), however nothing happens once I introduce TWO comparisons with the AND operator.

def next(self):
    if not self.position:
        if self.data.close[0] >= self.mov[0] and self.data.close[0] >= self.boll.lines.top[0]:
            print(self.data.close[0], self.datetime.date(ago=0), self.datetime.time(ago=0))
    else:
        print('nothing to print! ')

I don't get an error, but nothing seems to output. Not even the else statement.

I figured it out, it looks like "DataResampler" was changed to "resampledata"

read more here: https://www.backtrader.com/blog/posts/2016-03-07-release-1.2.1.88/release-1.2.1.88/

The documentation here is possibly out of data -->https://www.backtrader.com/docu/data-resampling/data-resampling/

The code I used for resampling 1-minute data into 6 hour data (6 hours = 360 mins):

cerebro.resampledata(data, timeframe=bt.TimeFrame.Minutes, compression=360) # to weeks

I had a similar attribute error and I was also using Jupyter Notebook

Hey everyone!

running into an issue here attempting to load backtrader on Python 3.8.2:

AttributeError: module 'backtrader' has no attribute 'DataResampler'

My file has a unique name that wouldn't collide with other names, any idea what is going on here?

Thanks!

import os, sys, argparse
import pandas as pd
import backtrader as bt
import backtrader.feeds as btfeeds
import backtrader.indicators as btindicators
import datetime
import matplotlib as plt
import math

%matplotlib inline

cerebro = bt.Cerebro()
cerebro.broker.setcash(1000)

     
class GoldenCross(bt.Strategy):
    params = (('fast', 50), ('slow', 200), ('order_percentage', 0.95), ('ticker', 'ETHUSD'))
    
    def __init__(self):
        self.fast_moving_average = bt.indicators.SMA(self.data.close, period=self.params.fast, plotname='50 dma')

        self.slow_moving_average = bt.indicators.SMA(self.data.close, period=self.params.slow, plotname='200 dma')
        
        self.crossover = bt.indicators.CrossOver(self.fast_moving_average, self.slow_moving_average)
        
        def next(self):
            if self.position.size == 0:
                if self.crossover > 0:
                    self.buy(size=1000)
                    
            elif self.position.size > 0:
                if self.crossover < 0:
                    print('SELL {} shares of {} at {}'.format(self.size, self.params.ticker, self.data.close[0]))
                    self.close()
                        
data = btfeeds.GenericCSVData(
    dataname=r'C:\Users\steve\Desktop\ethusd.csv',

    fromdate=datetime.datetime(2020, 1, 1),
    todate=datetime.datetime(2020, 5, 17),

    nullvalue=0.0,
    dtformat=('%Y-%m-%d %H:%M:%S'),
    datetime=0,
    high=3,
    low=4,
    open=2,
    close=5,
    volume=7,
    openinterest=-1
)

# Handy dictionary for the argument timeframe conversion
tframes = dict(
    daily=bt.TimeFrame.Days,
    weekly=bt.TimeFrame.Weeks,
    monthly=bt.TimeFrame.Months)

# Resample the data
data_resampled = bt.DataResampler(
    dataname=data,
    timeframe=tframes[args.timeframe],
    compression=args.compression)
    
    
cerebro.adddata(data_resampled)
cerebro.addstrategy(GoldenCross)
cerebro.run()

cerebro.plot()