Many thanks, much appreciated!

Dear all,

I would like to incorporate the supertrend indicator to backtrader (function is shown below). I'm new to backtrader / python, and I'm not sure how to code it to make it work properly with a calling like this:

self.supertrend = supertrend(self.datas)

Would be much appreciated if someone could provide guidance on how to implement it.

Many thanks in advance!

import backtrader as bt
import numpy as np
from pandas import DataFrame
import talib.abstract as ta

def supertrend(dataframe, multiplier=3, period=10):
    df = dataframe.copy()

    df['TR'] = ta.TRANGE(df)
    df['ATR'] = df['TR'].ewm(alpha=1 / period).mean()

    # atr = 'ATR_' + str(period)
    st = 'ST_' + str(period) + '_' + str(multiplier)
    stx = 'STX_' + str(period) + '_' + str(multiplier)

    # Compute basic upper and lower bands
    df['basic_ub'] = (df['high'] + df['low']) / 2 + multiplier * df['ATR']
    df['basic_lb'] = (df['high'] + df['low']) / 2 - multiplier * df['ATR']

    # Compute final upper and lower bands
    df['final_ub'] = 0.00
    df['final_lb'] = 0.00
    for i in range(period, len(df)):
        df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or \
                                                         df['close'].iat[i - 1] > df['final_ub'].iat[i - 1] else \
        df['final_ub'].iat[i - 1]
        df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or \
                                                         df['close'].iat[i - 1] < df['final_lb'].iat[i - 1] else \
        df['final_lb'].iat[i - 1]

    # Set the Supertrend value
    df[st] = 0.00
    for i in range(period, len(df)):
        df[st].iat[i] = df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[
            i] <= df['final_ub'].iat[i] else \
            df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] > \
                                     df['final_ub'].iat[i] else \
                df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= \
                                         df['final_lb'].iat[i] else \
                    df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] < \
                                             df['final_lb'].iat[i] else 0.00

        # Mark the trend direction up/down
    df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN)

    # Remove basic and final bands from the columns
    df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)

    df.fillna(0, inplace=True)

    return DataFrame(index=df.index, data={
        'ST': df[st],
        'STX': df[stx]
    })


class TestStrategy(bt.Strategy):

    def log(self, txt, dt=None):
        date = self.data.datetime.datetime()
        print('%s, %s' % (date, txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close
        self.dataopen = self.datas[0].open
        self.datahigh = self.datas[0].high
        self.datalow = self.datas[0].low
        self.datavolume = self.datas[0].volume

        # To keep track of pending orders and buy price/commission
        self.order = None

        self.supertrend = supertrend(self.datas)

Great thanks, exactly what I needed.

I just changed the buy / sell signals as per the below:

        if self.rsi[0] <= (np.percentile([self.rsiLLV[0], self.rsiHHV[0]], self.p.threshold_Buy)):
            self.order = self.buy()

 else:

        if self.rsi[0] >= (np.percentile([self.rsiLLV[0], self.rsiHHV[0]], self.p.threshold_Sell)):
            self.order = self.sell()

Many thanks again

Hi everyone,

I am fairly new to Python and Backtrader. I am testing out a simple RSI cross over strategy. Instead of having two hard-plugged thresholds for buy and sell signals, I’d like those thresholds to be moving across time series. For example, a buy signal would be triggered when current RSI is equal to the 10% minimum / lowest RSI over the last 24 hours, conversely a sell signal would be triggered when current RSI reaches the 90% highest RSI over the same period of time.

As you can see on the code below, I haven’t been successful figuring out what to code in order to get those moving thresholds (10% lowest and 90% highest thresholds) that would trigger a trade signal.

Any assistance or feedback would be greatly appreciated.

class TestStrategy(bt.Strategy):

params = (
    ('period',195),('threshold_Buy', 50),('threshold_Sell', 56),
    ) #tresholds here are hard plugged, the intent would be to have them as a percentage (for example 10% buy signal and 90% sell signal)

def log(self, txt, dt=None):
    dt = dt or self.datas[0].datetime.date(0)
    print('%s, %s' % (dt.isoformat(), txt))

def __init__(self):
    self.dataclose = self.datas[0].close
    self.order = None
    self.buyprice = None
    self.buycomm = None

    self.rsi = bt.indicators.RelativeStrengthIndex(self.datas[0], period=self.params.period) # simple RSI, period here has already been optimized for this particular stock

    self.smaRSI = bt.indicators.SimpleMovingAverage(self.rsi, period=288) #period here represents 24 hours cut into 5 minutes timeframe periods

def notify_order(self, order):
    if order.status in [order.Submitted, order.Accepted]:
        # Buy/Sell order submitted/accepted to/by broker - Nothing to do
        return

    if order.status in [order.Completed]:
        if order.isbuy():
            self.log(
                'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                (order.executed.price,
                 order.executed.value,
                 order.executed.comm))

            self.buyprice = order.executed.price
            self.buycomm = order.executed.comm
        else:
            self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                     (order.executed.price,
                      order.executed.value,
                      order.executed.comm))

        self.bar_executed = len(self)

    elif order.status in [order.Canceled, order.Margin, order.Rejected]:
        '''self.log('Order Canceled/Margin/Rejected')'''

    self.order = None

def notify_trade(self, trade):
    if not trade.isclosed:
        return

    self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
             (trade.pnl, trade.pnlcomm))

def next(self):

    if self.order:
        return

    if not self.position:

        print('rsi', self.rsi[0], 'smaRSI', self.smaRSI[0])

        if self.rsi[0] < self.params.threshold_Buy:
            self.order = self.buy()

    else:

        if self.rsi[0] > self.params.threshold_Sell:
            self.order = self.sell()