Best posts made by dasch

i fixed issues with my datafeeds by adjusting the time to the end of the period they are on. Backtrader expects somehow the endtime of a period, while most data will have the starttime of a period as the datetime.

A custom csv feed with time adjustment which can be used to fix that:

import backtrader as bt
from backtrader.utils import date2num
from datetime import datetime, time, timedelta



def getstarttime(timeframe, compression, dt, sessionstart=None, offset=0):
    '''
    This method will return the start of the period based on current
    time (or provided time).
    '''
    if sessionstart is None:
        # use UTC 22:00 (5:00 pm New York) as default
        sessionstart = time(hour=22, minute=0, second=0)
    if dt is None:
        dt = datetime.utcnow()
    if timeframe == bt.TimeFrame.Seconds:
        dt = dt.replace(
            second=(dt.second // compression) * compression,
            microsecond=0)
        if offset:
            dt = dt - timedelta(seconds=compression*offset)
    elif timeframe == bt.TimeFrame.Minutes:
        if compression >= 60:
            hours = 0
            minutes = 0
            # get start of day
            dtstart = getstarttime(bt.TimeFrame.Days, 1, dt, sessionstart)
            # diff start of day with current time to get seconds
            # since start of day
            dtdiff = dt - dtstart
            hours = dtdiff.seconds//((60*60)*(compression//60))
            minutes = compression % 60
            dt = dtstart + timedelta(hours=hours, minutes=minutes)
        else:
            dt = dt.replace(
                minute=(dt.minute // compression) * compression,
                second=0,
                microsecond=0)
        if offset:
            dt = dt - timedelta(minutes=compression*offset)
    elif timeframe == bt.TimeFrame.Days:
        if dt.hour < sessionstart.hour:
            dt = dt - timedelta(days=1)
        if offset:
            dt = dt - timedelta(days=offset)
        dt = dt.replace(
            hour=sessionstart.hour,
            minute=sessionstart.minute,
            second=sessionstart.second,
            microsecond=sessionstart.microsecond)
    elif timeframe == bt.TimeFrame.Weeks:
        if dt.weekday() != 6:
            # sunday is start of week at 5pm new york
            dt = dt - timedelta(days=dt.weekday() + 1)
        if offset:
            dt = dt - timedelta(days=offset * 7)
        dt = dt.replace(
            hour=sessionstart.hour,
            minute=sessionstart.minute,
            second=sessionstart.second,
            microsecond=sessionstart.microsecond)
    elif timeframe == bt.TimeFrame.Months:
        if offset:
            dt = dt - timedelta(days=(min(28 + dt.day, 31)))
        # last day of month
        last_day_of_month = dt.replace(day=28) + timedelta(days=4)
        last_day_of_month = last_day_of_month - timedelta(
            days=last_day_of_month.day)
        last_day_of_month = last_day_of_month.day
        # start of month
        if dt.day < last_day_of_month:
            dt = dt - timedelta(days=dt.day)
        dt = dt.replace(
            hour=sessionstart.hour,
            minute=sessionstart.minute,
            second=sessionstart.second,
            microsecond=sessionstart.microsecond)
    return dt


class CSVAdjustTime(bt.feeds.GenericCSVData):

    params = dict(
        adjstarttime=False,
    )

    def _loadline(self, linetokens):
        res = super(CSVAdjustTime, self)._loadline(linetokens)
        if self.p.adjstarttime:
            # move time to start time of next candle
            # and subtract 0.1 miliseconds (ensures no
            # rounding issues, 10 microseconds is minimum)
            new_date = getstarttime(
                self._timeframe,
                self._compression,
                self.datetime.datetime(0),
                self.sessionstart,
                -1) - timedelta(microseconds=100)
            self.datetime[0] = date2num(new_date)
        return res

If anyone needs this, here is a BBSqueeze indicator.

implementation is based on this: https://www.netpicks.com/squeeze-out-the-chop/

BBSqueeze

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import backtrader as bt

from . import KeltnerChannel


class BBSqueeze(bt.Indicator):

    '''
    https://www.netpicks.com/squeeze-out-the-chop/

    Both indicators are symmetrical, meaning that the upper and lower bands or channel lines are the same distance from the moving average. That means that we can focus on only one side in developing our indicator. In our case, we’ll just consider the upper lines.

    The basic formulas we need are:

        Bollinger Band = Moving Average + (Number of standard deviations X Standard Deviation)
        Keltner Channel = Moving Average + (Number of ATR’s X ATR)

    Or if we translate this into pseudo-code:

        BBUpper = Avg(close, period) + (BBDevs X StdDev(close, period))
        KCUpper = Avg(close, period) + (KCDevs X ATR(period))

    The squeeze is calculated by taking the difference between these two values:

        Squeeze = BBUpper – KCUpper

    Which simplifies down to this:

        Squeeze = (BBDevs X StdDev(close, period)) – (KCDevs X ATR(period))
    '''

    lines = ('squeeze',)
    params = (('period', 20), ('devfactor', 2.0), ('movav', bt.ind.MovAv.Simple),)

    plotinfo = dict(subplot=True)

    def _plotlabel(self):
        plabels = [self.p.period, self.p.devfactor]
        plabels += [self.p.movav] * self.p.notdefault('movav')
        return plabels

    def __init__(self):
        bb = bt.ind.BollingerBands(
            period=self.p.period, devfactor=self.p.devfactor, movav=self.p.movav)
        kc = KeltnerChannel(
            period=self.p.period, devfactor=self.p.devfactor, movav=self.p.movav)
        self.lines.squeeze = bb.top - kc.top

KeltnerChannel

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import backtrader as bt


class KeltnerChannel(bt.Indicator):

    lines = ('mid', 'top', 'bot',)
    params = (('period', 20), ('devfactor', 1.5),
              ('movav', bt.ind.MovAv.Simple),)

    plotinfo = dict(subplot=False)
    plotlines = dict(
        mid=dict(ls='--'),
        top=dict(_samecolor=True),
        bot=dict(_samecolor=True),
    )

    def _plotlabel(self):
        plabels = [self.p.period, self.p.devfactor]
        plabels += [self.p.movav] * self.p.notdefault('movav')
        return plabels

    def __init__(self):
        self.lines.mid = ma = self.p.movav(self.data, period=self.p.period)
        atr = self.p.devfactor * bt.ind.ATR(self.data, period=self.p.period)
        self.lines.top = ma + atr
        self.lines.bot = ma - atr

@curious_one your datetime is a string, try converting it to datetime objects. Look at pandas to_datetime() method.

df.Datetime = pandas.to_datetime(
            df.Datetime, format='%Y-%m-%d  %H:%M:%S')

@tsguo3 if you want to be sure, you can always read the source code.

BBSqueeze with configureable multiplicators for bb and keltner

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import backtrader as bt

from . import KeltnerChannel


class BBSqueeze(bt.Indicator):

    '''
    https://www.netpicks.com/squeeze-out-the-chop/

    Both indicators are symmetrical, meaning that the upper and lower bands or channel lines are the same distance from the moving average. That means that we can focus on only one side in developing our indicator. In our case, we’ll just consider the upper lines.

    The basic formulas we need are:

        Bollinger Band = Moving Average + (Number of standard deviations X Standard Deviation)
        Keltner Channel = Moving Average + (Number of ATR’s X ATR)

    Or if we translate this into pseudo-code:

        BBUpper = Avg(close, period) + (BBDevs X StdDev(close, period))
        KCUpper = Avg(close, period) + (KCDevs X ATR(period))

    The squeeze is calculated by taking the difference between these two values:

        Squeeze = BBUpper – KCUpper

    Which simplifies down to this:

        Squeeze = (BBDevs X StdDev(close, period)) – (KCDevs X ATR(period))
    '''

    lines = ('squeeze',)
    params = (('period', 20), ('bbdevs', 2.0), ('kcdevs', 1.5), ('movav', bt.ind.MovAv.Simple),)

    plotinfo = dict(subplot=True)

    def _plotlabel(self):
        plabels = [self.p.period, self.p.bbdevs, self.p.kcdevs]
        plabels += [self.p.movav] * self.p.notdefault('movav')
        return plabels

    def __init__(self):
        bb = bt.ind.BollingerBands(
            period=self.p.period, devfactor=self.p.bbdevs, movav=self.p.movav)
        kc = KeltnerChannel(
            period=self.p.period, devfactor=self.p.kcdevs, movav=self.p.movav)
        self.lines.squeeze = bb.top - kc.top

Another indicator, Chandelier Exit

import backtrader as bt


class ChandelierExit(bt.Indicator):

    ''' https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/chandelier-exit/ '''

    lines = ('long', 'short')
    params = (('period', 22), ('multip', 3),)

    plotinfo = dict(subplot=False)

    def __init__(self):
        highest = bt.ind.Highest(self.data.high, period=self.p.period)
        lowest = bt.ind.Lowest(self.data.low, period=self.p.period)
        atr = self.p.multip * bt.ind.ATR(self.data, period=self.p.period)
        self.lines.long = highest - atr
        self.lines.short = lowest + atr

@hfrog713 said in Converting a Tradingview Strategy to a Backtrader Strategy - EMA Crossover:

def next(self):
    if self.position.size:
        if self.crossover < 0:
            self.sell()
    elif self.crossover > 0:
        self.buy()

Here is a indention error, the elif self.crossover > 0

I fixed the issue with oanda broker. It will now assume, there is no stopside

When you create the datafeed, you need to specify the timeframe and compression your data is using. Backtrader will not identify it by itself.

So when you do:

 data_feed = CryptoPandasData(dataname=time_bars)

you would do it that way:

 data_feed = CryptoPandasData(dataname=time_bars, timeframe=bt.TimeFrame.Minutes, compression=1)

Hi

I have worked on converting a Pinescript indicator to backtrader.

I will describe some findings in the process, give some tips, show a example and so on.

variables
in pinescript, variables seem to work like lines in backtrader. So for every variable used in the script I have added a line:

pinescript:

setupCountUp = na

backtrader ind.__init__:

self.setup_sell = bt.LineNum(float('nan'))

conditions
to use methods like barssince, valuewhen from pinescript, which allows conditions like: setupCountUp == self.p.setup_bars the easiest method I found is to use numpy for this.

1. convert a line to a numpy array (using get(size=SIZE) to not work on complete line but only a defined range of data)

def line2arr(line, size=-1):
    if size <= 0:
        return np.array(line.array)
    else:
        return np.array(line.get(size=size))

2. provide methods which works as methods from pinescript: so I wrote the methods I needed.

def na(val):
    return val != val

def nz(x, y=None):
    if isinstance(x, np.generic):
        return x.fillna(y or 0)
    if x != x:
        if y is not None:
            return y
        return 0
    return x

def barssince(condition, occurrence=0):
    cond_len = len(condition)
    occ = 0
    since = 0
    res = float('nan')
    while cond_len - (since+1) >= 0:
        cond = condition[cond_len-(since+1)]
        if cond and not cond != cond:
            if occ == occurrence:
                res = since
                break
            occ += 1
        since += 1
    return res

def valuewhen(condition, source, occurrence=0):
    res = float('nan')
    since = barssince(condition, occurrence)
    if since is not None:
        res = source[-(since+1)]
    return res

rewriting

the process of rewriting the pinescript in python using backtrader was simple after this. basically it can be split up to:

1. look for vars, params, signals used in pinescript
2. define vars, params, signal in indicator
3. strip all plot related code from pinescript
4. rewrite pinescript code in indicators next method

plotting

since i only want to have signals as lines in indicator, I did not set all vars used in pinescript in the lines dict, but in init. So I had a better control of what the indicator is plotting. But the indicator had more values then just the signals, so how to handle them?
The solution was a observer, which takes care of plotting all other information, the indicator has. Here you can setup all the plot related code from pinescript.

some remaining issues

maybe some of you guys know answers for this:

Questions:

• is it possible to create lines with conditions (outside of __init__) like: self.line1 > val ?
• is there a way to get something like barssince, valuewhen natively in backtrader?

Issues:

• the code is not very good, since a lot of the code can be done better in python, but if you don't want to fiddle to much with it, this should be fine
• refactoring would make the code better readable

I hope, this will help some of you!
I will post the code of a indicator, observer and the pinescript methods in a next post below.

by using the adjustment above, no need to alter any settings like

        rightedge=True,
        bar2edge=True,
        adjbartime=True,
        boundoff=0,

@Nam-Nguyen for the tick question, you will not experience any issues with backtrader when using tick data, since they are just events at a given time. they will resample correctly

@Wayne-Filkins I checked the code for alpaca data. the data feeds support backfill. Check out the params:

      - ``backfill_start`` (default: ``True``)
        Perform backfilling at the start. The maximum possible historical data
        will be fetched in a single request.
      - ``backfill`` (default: ``True``)
        Perform backfilling after a disconnection/reconnection cycle. The gap
        duration will be used to download the smallest possible amount of data
      - ``backfill_from`` (default: ``None``)
        An additional data source can be passed to do an initial layer of
        backfilling. Once the data source is depleted and if requested,
        backfilling from IB will take place. This is ideally meant to backfill
        from already stored sources like a file on disk, but not limited to.

I did not look up what the size of max possible historical data per request is.

But it will backfill by default on live trading. You should check out their api description to see, what size it will be (how many historical candles it will prefill on start).

@Nam-Nguyen you can use my code with the monkey patching of _load to have a common solution for this.

@run-out if the metatrader datafeeds have a datetime which is the start of the period then my solution will work with metatrader datafeeds

you should check the length of data, if it really did advance.

next will be called on every data source. so if the 5 minute data advances, next will be called (this is why between 4 and 6, 4 will be twice.

the datetime of the resampled data is the end of the period, there you may use the settings

        rightedge=True,
        bar2edge=True,
        adjbartime=True,
        boundoff=0,

to adjust the time to be the start of a period

when using multiple data sources, you will get notified of every change. when resampling, if data advances, on replay if data changes (in that case, data will not necessary advance). So to know if the data source advanced, you need to check the length of data.

example:

if self._last_len > len(self.datas[1]):
    self.log(self.datas[1].close[0])
self._last_len = len(self.datas[1]

here is a cleaned up version of the indicator.

Trend_Indicator(bt.Indicator):
    lines = ('trend',)
    params = (
        ('slow_ema', 21),
        ('fast_ema', 8),
    )

    def __init__(self):

        # Add Bollinger bands
        self.bb = bt.indicators.BollingerBands()

        self.slow_ema = bt.indicators.EMA(period=self.params.slow_ema)
        self.fast_ema = bt.indicators.EMA(period=self.params.fast_ema)

        self.ohlc = {"o": None, "h": None, "l": None, "c": None}

        self.trend_bollinger_touch = 0
        self.trend_status = 0
        self.trend_enter = 0
        self.trend_exit_params = {
            "20BarRule": False,
            "EMARule": False,
            "OppositeBollingerTouch": False}
        self.trend_ema_rule = {
            "Close1": False,
            "Close2": False}
        self.trend_position = 0
        self.trend_test = 0

    def nextstart(self):
        self._update_ohlc()

    def next(self):
        if (self.trend_bollinger_touch == 1
                and self.data.low[0] > self.bb.lines.bot[0]
                and self.trend_enter == 0):
            # if the upper bollinger band was touched and the candle
            # we have now makes the highest high and highest close,
            # we enter in an uptrend
            if (self.data.high[0] > self.ohlc["h"]
                    and self.data.close[0] > self.ohlc["c"]):
                self._update_ohlc(["h"])
                self.trend_enter = 1
                self.trend_status = 1
                self.trend_exit_params["OppositeBollingerTouch"] = False
                self.trend_exit_params["20BarRule"] = False
                self.trend_exit_params["EMARule"] = False
                self.trend_position = len(self.ohlc)
                self.trend_test = len(self.data.high)
            if self.data.high[0] > self.ohlc["h"]:
                self._update_ohlc(["h"])
            if self.data.close[0] > self.ohlc["c"]:
                self._update_ohlc(["c"])

        # we enter in a downtrend
        elif (self.trend_bollinger_touch == -1
                and self.data.high[0] < self.bb.lines.top[0]
                and self.trend_enter == 0):
            # if the bottom bollinger band was touched and the candle we
            # have now makes the lowest low and lowest close, we enter
            # in a downtrend
            if (self.data.low[0] < self.ohlc["l"]
                    and self.data.close[0] < self.ohlc["c"]):
                self._update_ohlc(["l"])
                self.trend_enter = -1
                self.trend_status = -1
                self.trend_exit_params["OppositeBollingerTouch"] = False
                self.trend_exit_params["20BarRule"] = False
                self.trend_exit_params["EMARule"] = False
                self.trend_position = len(self.ohlc)
                self.trend_test = len(self.data.low)
            if self.data.low[0] < self.ohlc["l"]:
                self._update_ohlc(["l"])
            if self.data.close[0] < self.ohlc["c"]:
                self._update_ohlc(["c"])

        elif self.trend_bollinger_touch == 0:
            # look for a top bollinger touch, then store that bar's data
            if (self.data.high[0] >= self.bb.lines.top[0]
                    and self.data.low[0] > self.bb.lines.bot[0]):
                self._update_ohlc()
                self.trend_bollinger_touch = 1

            elif (self.data.low[0] <= self.bb.lines.bot[0]
                    and self.data.high[0] < self.bb.lines.top[0]):
                self._update_ohlc()
                self.trend_bollinger_touch = -1

        # if the upper bollinger band was touched but now the lower bollinger
        # band got touched, the upper bollinger touch resets
        if (self.trend_bollinger_touch == 1
                and self.data.low[0] <= self.bb.lines.bot[0]):
            self._update_ohlc()
            self.trend_bollinger_touch = -1

        # if the bottom bollinger band was touched but now the top bollinger
        # band got touched, the bottom bollinger touch resets
        elif (self.trend_bollinger_touch == -1
                and self.data.high[0] >= self.bb.lines.top[0]):
            self._update_ohlc()
            self.trend_bollinger_touch = 1

        # we are in an uptrend
        if self.trend_status == 1:
            # if the opposite bollinger band was touched, we exit the uptrend
            if self.data.low[0] <= self.bb.lines.bot[0]:
                self.trend_status = 0
                self.trend_enter = 0
                self.trend_exit_params["OppositeBollingerTouch"] = True
                self.trend_bollinger_touch = -1
                self.trend_ema_rule['Close1'] = False
                self.trend_ema_rule['Close2'] = False
                self._update_ohlc()
            # if a fresh high is made, keep track of the position
            if self.data.high[0] > self.ohlc["h"]:
                self._update_ohlc(["h"])
                self.trend_position = len(self.data.high)
            # after 20 bars (excluding the first) no new high is made, we are
            # not in a trend anymore
            elif self.data.high[0] < self.ohlc["h"]:
                if len(self.data.high) - self.trend_position >= 21:
                    self.trend_status = 0
                    self.trend_enter = 0
                    self.trend_bollinger_touch = -1
                    self.trend_exit_params["20BarRule"] = True
                    self.trend_ema_rule['Close1'] = False
                    self.trend_ema_rule['Close2'] = False
                    self.trend_exit_params['20BarRule'] = True

            # if the top bollinger band was touched, reset the EMA rule
            if self.data.high[0] >= self.bb.lines.top[0]:
                self.trend_ema_rule['Close1'] = False
                self.trend_ema_rule['Close2'] = False

            if (self.data.close[0] < self.slow_ema[0]
                    and self.data.close[0] < self.fast_ema[0]):
                self.trend_ema_rule['Close1'] = True

            if (self.trend_ema_rule['Close1'] is True
                    and self.data.close[0] > self.slow_ema[0]
                    and self.data.close[0] > self.fast_ema[0]):
                self.trend_ema_rule['Close2'] = True

            if (self.trend_ema_rule['Close1'] is True
                    and self.trend_ema_rule['Close2'] is True
                    and self.data.close[0] < self.slow_ema[0]
                    and self.data.close[0] < self.fast_ema[0]):
                self.trend_ema_rule['Close1'] = False
                self.trend_ema_rule['Close2'] = False
                self.trend_exit_params['EMARule'] = True
                self.trend_enter = 0
                self.trend_status = 0
                self.trend_bollinger_touch = 0

        # we are in a downtrend
        if self.trend_status == -1:
            # if the opposite bollinger band was touched, we exit the downtrend
            if self.data.high[0] >= self.bb.lines.top[0]:
                self.trend_status = 0
                self.trend_enter = 0
                self.trend_bollinger_touch = 1
                self.trend_exit_params["OppositeBollingerTouch"] = True
                self.trend_ema_rule['Close1'] = False
                self.trend_ema_rule['Close2'] = False
                self._update_ohlc()

            # if a fresh low is made, keep track of the position
            if self.data.low[0] < self.ohlc["h"]:
                self._update_ohlc(["l"])
                self.trend_position = len(self.data.low)
            # after 20 bars (excluding the first) no new low is made, we are
            # not in a trend anymore
            elif self.data.low[0] > self.ohlc["l"]:
                if len(self.data.low) - self.trend_position >= 21:
                    self.trend_status = 0
                    self.trend_enter = 0
                    self.trend_bollinger_touch = 0
                    self.trend_exit_params["20BarRule"] = True
                    self.trend_ema_rule['Close1'] = False
                    self.trend_ema_rule['Close2'] = False
                    self.trend_exit_params['20BarRule'] = True

            # if the bottom bollinger band was touched, reset the EMA rule
            if self.data.low[0] <= self.bb.lines.bot[0]:
                self.trend_ema_rule['Close1'] = False
                self.trend_ema_rule['Close2'] = False

            if (self.data.close[0] > self.slow_ema[0]
                    and self.data.close[0] > self.fast_ema[0]):
                self.trend_ema_rule['Close1'] = True

            if (self.trend_ema_rule['Close1'] is True
                    and self.data.close[0] < self.slow_ema[0]
                    and self.data.close[0] < self.fast_ema[0]):
                self.trend_ema_rule['Close2'] = True

            if (self.trend_ema_rule['Close1'] is True
                    and self.trend_ema_rule['Close2'] is True
                    and self.data.close[0] > self.slow_ema[0]
                    and self.data.close[0] > self.fast_ema[0]):
                self.trend_status = 0
                self.trend_enter = 0
                self.trend_bollinger_touch = 0
                self.trend_ema_rule['Close1'] = False
                self.trend_ema_rule['Close2'] = False
                self.trend_exit_params['EMARule'] = True

        # update line
        if (self.trend_enter == -1
                and True not in self.trend_exit_params.values()):
            self.lines.trend[0] = -1
        elif (self.trend_enter == 1
                and True not in self.trend_exit_params.values()):
            self.lines.trend[0] = 1

    def _update_ohlc(self, values=["o", "h", "l", "c"]):
        if "o" in values:
            self.ohlc["o"] = self.data.open[0]
        if "h" in values:
            self.ohlc["h"] = self.data.high[0]
        if "l" in values:
            self.ohlc["l"] = self.data.low[0]
        if "c" in values:
            self.ohlc["c"] = self.data.close[0]

@ab_trader here: https://www.backtrader.com/docu/inddev/

Let’s recall that self.lines.dummyline is the long notation and that it can be shortened to:

self.l.dummyline

and even to:

self.dummyline

The latter being only possible if the code has not obscured this with a member attribute.

I noticed, that when using replaydata from a live data source, which is not added, will use the first data clock for other data sources.

So for example, if you have tick data, which is not added to cerebro, then you replaydata to some timeframe using the data and then adding another data source the same way, the second data source will only be updated, if the first replayed data source forwards.

some code to visualize this:

# create some data feed with live data
data = DataFeed()

# add replayed data
cerebro.replaydata(data, timeframe=bt.TimeFrame.Minutes, compression=1, name="primary")
cerebro.replaydata(data, timeframe=bt.TimeFrame.Minutes, compression=30, name="secondary")

This will go this way:

• data source primary will work as expected and will be updated every tick that comes in
• data source secondary will be updated when primary moves one candle forward (in the example above every 1 minute)

When adding data this way:

# create some data feed with live data
data = DataFeed()

# add live data
cerebro.adddata(data)
# add replayed data
cerebro.replaydata(data, timeframe=bt.TimeFrame.Minutes, compression=1, name="primary")
cerebro.replaydata(data, timeframe=bt.TimeFrame.Minutes, compression=30, name="secondary")

This will go this way:

• data source primary will work as expected and will be updated every tick that comes in
• data source secondary will work as expected and will be updated every tick that comes in

Just in case anyone comes into that situation when using replaydata

you need to know the pip location. the pip location says, where pips are after comma, for EUR_USD it would be 4. below you have two functions to calculate the price diff from pips and pips from a value.

    def get_pips_from_value(value, pip_location, precision=0):
        ''' Returns pips from a value '''
        div = float(10 ** pip_location)
        pips = value / div
        return round(pips, precision)

    def get_value_from_pips(pips, pip_location):
        ''' Returns price diff from pips '''
        mult = float(10 ** pip_location)
        return float(pips * mult)