Measuring Indicator and Data Memory Use in Backtrader
Summary
This Backtrader example demonstrates a way to inspect memory consumption during a strategy run. It builds a sample strategy with common indicators and a custom indicator, then counts stored data-line cells for feeds, indicators, and observers. Optional detailed logging breaks those counts down by component, while command-line settings select a memory-saving mode and can print data progress or plot the run.
The script offers a practical diagnostic for comparing how much line data a backtest retains under different configurations. It reports counts of stored cells rather than actual bytes, and the example does not provide comparative measurements or quantify runtime effects. It also uses sample Yahoo Finance CSV input and indicators, so results from this setup alone cannot establish memory use for another strategy or dataset. Treat it as an instrumentation example, and check its accounting logic before relying on totals.
Key ideas
- The example counts stored cells across data feeds, indicators, and observers.
- Recursive inspection can report memory-cell counts for nested indicator components.
- A command-line setting selects Backtrader’s memory-saving mode for the run.
- Cell counts are a proxy for storage, not a direct measurement of bytes or runtime cost.
Tags
Full text
# memory-savings.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 sys
import backtrader as bt
import backtrader.feeds as btfeeds
import backtrader.indicators as btind
import backtrader.utils.flushfile
class TestInd(bt.Indicator):
lines = ('a', 'b')
def __init__(self):
self.lines.a = b = self.data.close - self.data.high
self.lines.b = btind.SMA(b, period=20)
class St(bt.Strategy):
params = (
('datalines', False),
('lendetails', False),
)
def __init__(self):
btind.SMA()
btind.Stochastic()
btind.RSI()
btind.MACD()
btind.CCI()
TestInd().plotinfo.plot = False
def next(self):
if self.p.datalines:
txt = ','.join(
['%04d' % len(self),
'%04d' % len(self.data0),
self.data.datetime.date(0).isoformat()]
)
print(txt)
def loglendetails(self, msg):
if self.p.lendetails:
print(msg)
def stop(self):
super(St, self).stop()
tlen = 0
self.loglendetails('-- Evaluating Datas')
for i, data in enumerate(self.datas):
tdata = 0
for line in data.lines:
tdata += len(line.array)
tline = len(line.array)
tlen += tdata
logtxt = '---- Data {} Total Cells {} - Cells per Line {}'
self.loglendetails(logtxt.format(i, tdata, tline))
self.loglendetails('-- Evaluating Indicators')
for i, ind in enumerate(self.getindicators()):
tlen += self.rindicator(ind, i, 0)
self.loglendetails('-- Evaluating Observers')
for i, obs in enumerate(self.getobservers()):
tobs = 0
for line in obs.lines:
tobs += len(line.array)
tline = len(line.array)
tlen += tdata
logtxt = '---- Observer {} Total Cells {} - Cells per Line {}'
self.loglendetails(logtxt.format(i, tobs, tline))
print('Total memory cells used: {}'.format(tlen))
def rindicator(self, ind, i, deep):
tind = 0
for line in ind.lines:
tind += len(line.array)
tline = len(line.array)
thisind = tind
tsub = 0
for j, sind in enumerate(ind.getindicators()):
tsub += self.rindicator(sind, j, deep + 1)
iname = ind.__class__.__name__.split('.')[-1]
logtxt = '---- Indicator {}.{} {} Total Cells {} - Cells per line {}'
self.loglendetails(logtxt.format(deep, i, iname, tind, tline))
logtxt = '---- SubIndicators Total Cells {}'
self.loglendetails(logtxt.format(deep, i, iname, tsub))
return tind + tsub
def runstrat():
args = parse_args()
cerebro = bt.Cerebro()
data = btfeeds.YahooFinanceCSVData(dataname=args.data)
cerebro.adddata(data)
cerebro.addstrategy(
St, datalines=args.datalines, lendetails=args.lendetails)
cerebro.run(runonce=False, exactbars=args.save)
if args.plot:
cerebro.plot(style='bar')
def parse_args():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Check Memory Savings')
parser.add_argument('--data', required=False,
default='../../datas/yhoo-1996-2015.txt',
help='Data to be read in')
parser.add_argument('--save', required=False, type=int, default=0,
help=('Memory saving level [1, 0, -1, -2]'))
parser.add_argument('--datalines', required=False, action='store_true',
help=('Print data lines'))
parser.add_argument('--lendetails', required=False, action='store_true',
help=('Print individual items memory usage'))
parser.add_argument('--plot', required=False, action='store_true',
help=('Plot the result'))
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.