Comparing Backtrader and TA-Lib Technical Indicators
Code backtrader
Summary
This sample demonstrates how to configure a Backtrader strategy to display technical indicators from TA-Lib alongside corresponding Backtrader indicators. Options cover moving averages, stochastic, RSI, MACD, Bollinger Bands, Aroon, Ultimate Oscillator, TRIX, KAMA, ADXR, DEMA, PPO, TEMA, rate-of-change variants, and Williams %R. It can also add a Doji candlestick detector. The selected indicator is controlled by a command-line option, and the example loads Yahoo Finance CSV data for a chosen date range.
The sample is aimed at indicator setup and visual comparison, with plotting available for inspection. It contains no trading entries or exits, benchmark, or backtest results, so it does not establish that any indicator or combination is profitable. Its evidence is limited to showing which library calls and data inputs are used; users would need separate analysis to assess calculation differences or trading value.
Key ideas
- The sample pairs TA-Lib indicators with analogous Backtrader indicators for visual inspection.
- It supports a broad selection of trend, momentum, oscillator, and volatility indicators.
- A Doji candlestick detector can be included with the selected indicator.
- Historical data can be loaded from a CSV over a specified date range.
- The example visualizes indicators but does not test a trading strategy.
Tags
Full text
# talibtest.py
```py
#!/usr/bin/env python
# -*- coding: utf-8; py-indent-offset:4 -*-
###############################################################################
#
# Copyright (C) 2015-2023 Daniel Rodriguez
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
###############################################################################
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import argparse
import datetime
import backtrader as bt
class TALibStrategy(bt.Strategy):
params = (('ind', 'sma'), ('doji', True),)
INDS = ['sma', 'ema', 'stoc', 'rsi', 'macd', 'bollinger', 'aroon',
'ultimate', 'trix', 'kama', 'adxr', 'dema', 'ppo', 'tema',
'roc', 'williamsr']
def __init__(self):
if self.p.doji:
bt.talib.CDLDOJI(self.data.open, self.data.high,
self.data.low, self.data.close)
if self.p.ind == 'sma':
bt.talib.SMA(self.data.close, timeperiod=25, plotname='TA_SMA')
bt.indicators.SMA(self.data, period=25)
elif self.p.ind == 'ema':
bt.talib.EMA(timeperiod=25, plotname='TA_SMA')
bt.indicators.EMA(period=25)
elif self.p.ind == 'stoc':
bt.talib.STOCH(self.data.high, self.data.low, self.data.close,
fastk_period=14, slowk_period=3, slowd_period=3,
plotname='TA_STOCH')
bt.indicators.Stochastic(self.data)
elif self.p.ind == 'macd':
bt.talib.MACD(self.data, plotname='TA_MACD')
bt.indicators.MACD(self.data)
bt.indicators.MACDHisto(self.data)
elif self.p.ind == 'bollinger':
bt.talib.BBANDS(self.data, timeperiod=25,
plotname='TA_BBANDS')
bt.indicators.BollingerBands(self.data, period=25)
elif self.p.ind == 'rsi':
bt.talib.RSI(self.data, plotname='TA_RSI')
bt.indicators.RSI(self.data)
elif self.p.ind == 'aroon':
bt.talib.AROON(self.data.high, self.data.low, plotname='TA_AROON')
bt.indicators.AroonIndicator(self.data)
elif self.p.ind == 'ultimate':
bt.talib.ULTOSC(self.data.high, self.data.low, self.data.close,
plotname='TA_ULTOSC')
bt.indicators.UltimateOscillator(self.data)
elif self.p.ind == 'trix':
bt.talib.TRIX(self.data, timeperiod=25, plotname='TA_TRIX')
bt.indicators.Trix(self.data, period=25)
elif self.p.ind == 'adxr':
bt.talib.ADXR(self.data.high, self.data.low, self.data.close,
plotname='TA_ADXR')
bt.indicators.ADXR(self.data)
elif self.p.ind == 'kama':
bt.talib.KAMA(self.data, timeperiod=25, plotname='TA_KAMA')
bt.indicators.KAMA(self.data, period=25)
elif self.p.ind == 'dema':
bt.talib.DEMA(self.data, timeperiod=25, plotname='TA_DEMA')
bt.indicators.DEMA(self.data, period=25)
elif self.p.ind == 'ppo':
bt.talib.PPO(self.data, plotname='TA_PPO')
bt.indicators.PPO(self.data, _movav=bt.indicators.SMA)
elif self.p.ind == 'tema':
bt.talib.TEMA(self.data, timeperiod=25, plotname='TA_TEMA')
bt.indicators.TEMA(self.data, period=25)
elif self.p.ind == 'roc':
bt.talib.ROC(self.data, timeperiod=12, plotname='TA_ROC')
bt.talib.ROCP(self.data, timeperiod=12, plotname='TA_ROCP')
bt.talib.ROCR(self.data, timeperiod=12, plotname='TA_ROCR')
bt.talib.ROCR100(self.data, timeperiod=12, plotname='TA_ROCR100')
bt.indicators.ROC(self.data, period=12)
bt.indicators.Momentum(self.data, period=12)
bt.indicators.MomentumOscillator(self.data, period=12)
elif self.p.ind == 'williamsr':
bt.talib.WILLR(self.data.high, self.data.low, self.data.close,
plotname='TA_WILLR')
bt.indicators.WilliamsR(self.data)
def runstrat(args=None):
args = parse_args(args)
cerebro = bt.Cerebro()
dkwargs = dict()
if args.fromdate:
fromdate = datetime.datetime.strptime(args.fromdate, '%Y-%m-%d')
dkwargs['fromdate'] = fromdate
if args.todate:
todate = datetime.datetime.strptime(args.todate, '%Y-%m-%d')
dkwargs['todate'] = todate
data0 = bt.feeds.YahooFinanceCSVData(dataname=args.data0, **dkwargs)
cerebro.adddata(data0)
cerebro.addstrategy(TALibStrategy, ind=args.ind, doji=not args.no_doji)
cerebro.run(runcone=not args.use_next, stdstats=False)
if args.plot:
pkwargs = dict(style='candle')
if args.plot is not True: # evals to True but is not True
npkwargs = eval('dict(' + args.plot + ')') # args were passed
pkwargs.update(npkwargs)
cerebro.plot(**pkwargs)
def parse_args(pargs=None):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Sample for sizer')
parser.add_argument('--data0', required=False,
default='../../datas/yhoo-1996-2015.txt',
help='Data to be read in')
parser.add_argument('--fromdate', required=False,
default='2005-01-01',
help='Starting date in YYYY-MM-DD format')
parser.add_argument('--todate', required=False,
default='2006-12-31',
help='Ending date in YYYY-MM-DD format')
parser.add_argument('--ind', required=False, action='store',
default=TALibStrategy.INDS[0],
choices=TALibStrategy.INDS,
help=('Which indicator pair to show together'))
parser.add_argument('--no-doji', required=False, action='store_true',
help=('Remove Doji CandleStick pattern checker'))
parser.add_argument('--use-next', required=False, action='store_true',
help=('Use next (step by step) '
'instead of once (batch)'))
# Plot options
parser.add_argument('--plot', '-p', nargs='?', required=False,
metavar='kwargs', const=True,
help=('Plot the read data applying any kwargs passed\n'
'\n'
'For example (escape the quotes if needed):\n'
'\n'
' --plot style="candle" (to plot candles)\n'))
if pargs is not None:
return parser.parse_args(pargs)
return parser.parse_args()
if __name__ == '__main__':
runstrat()
```Shown in full with attribution under the source's licence. Licence: GPL-3.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.