backtrader-next 2.3.5

Live Trading and backtesting platform in Python

backtrader-next

Live Trading and backtesting platform written in Python.

Installation

pip install backtrader-next

History

Package is based on backtrader

Changes:

• Added new Chart plotting using bn-lightweight-charts-python.
• Improved testing performance by using the PandasData feed in runonce=True mode.
• Added performance statistics in both text format (similar to Backtesting.py) and HTML format (similar to Quantstats).
• Improved support for switching between futures (for testing, etc.).
• Added new indicators implemented with Numba.
• Improved performance — now it runs about 2–3× slower than Backtesting.py in runonce=True mode with PandasData.
• Detailed results
• Interactive visualizations

Performance

Performance comparison using the perf_compare benchmark.

• Backtrader-next using an optimized PandasData feed
• Backtrader with PandasData feed
• Backtesting.py

Framework	Execution Time	Relative Speed
Backtesting	2.95 sec	14.3x faster than Backtrader
Backtrader-next	12.33 sec	3.4x faster than Backtrader
Backtrader	42.25 sec	Baseline

Here a snippet of a Simple Moving Average CrossOver.

import pandas as pd
import backtrader_next as bt
from backtrader_next.feeds import PandasData


class SimpleSizer(bt.Sizer):
    params = (
        ('percents', 99),
    )

    def _getsizing(self, comminfo, cash, data, isbuy):
        value = self.broker.getvalue()
        price = data.close[0]+comminfo.p.commission
        size = value / price * (self.p.percents / 100)
        return int(size)


class SmaCross(bt.Strategy):
    params = (
        ('MA1', 20),
        ('MA2', 50),
    )

    def __init__(self):
        self.Order = None
        self.ma1 = bt.nind.SMA(self.data.close, period=self.p.MA1)
        self.ma2 = bt.nind.SMA(self.data.close, period=self.p.MA2)


    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:  # Order is submitted/accepted
            return  # Do nothing until the order is completed

        # if order.status in [order.Completed]:  # Order is completed
        #     if order.isbuy():  # Buy order
        #         pass
        #     elif order.issell():  # Sell order 
        #         pass

        elif order.status in [order.Canceled]:  # Canceled, Margin, Rejected
            print('Order was Canceled', self.data.datetime.datetime(0))

        elif order.status in [order.Margin]:  # Canceled, Margin, Rejected
            print('Order was Margin ', self.data.datetime.datetime(0))

        elif order.status in [order.Rejected]:  # Canceled, Margin, Rejected
            print('Order was Rejected', self.data.datetime.datetime(0))

        self.Order = None  # Reset order



    def next(self):
        # Use ONLY Long Positions
        if self.crossover(self.ma1, self.ma2):
            pos = self.getposition()
            if pos:
                self.close(size=pos.size)
            self.Order = self.buy()
        elif self.crossover(self.ma2, self.ma1):
            pos = self.getposition()
            if pos:
                self.close(size=pos.size)
            # self.Order = self.sell()

    def crossover(self, ma1, ma2):
        try:
            return ma1[-1] <= ma2[-1] and ma1[0] > ma2[0]
        except IndexError:
            return False



if __name__ == '__main__':
    cerebro = bt.Cerebro()
    cerebro.broker.setcash(1_000_000.0)
    cerebro.broker.set_shortcash(False)
    cerebro.broker.setcommission(commission=0, margin=False)
    cerebro.addsizer(SimpleSizer, percents=90)

    df = pd.read_csv(f"AAPL_1d.csv.zip", sep=";")
    df['Datetime'] = pd.to_datetime(df['Date'].astype(str) , format='%Y-%m-%d')
    df.set_index('Datetime', inplace=True)

    data = PandasData(dataframe=df, timeframe=bt.TimeFrame.Days, compression=1)
    cerebro.adddata(data, name='AAPL')

    cerebro.addstrategy(SmaCross, )

    print(f'Starting Portfolio Value: {cerebro.broker.getvalue():.2f}\n')
    results = cerebro.run()
    print(f'\nFinal Portfolio Value: {cerebro.broker.getvalue():.2f}\n')

    rc = cerebro.statistics
    print(rc)

    # old plot required matplotlib
    # cerebro.old_plot(style='candle')
    
    cerebro.plot(filename="smacross.html")
    cerebro.show_report(filename="smacross_stats.html")
    print("end")

Output log

Starting Portfolio Value: 1000000.00


Final Portfolio Value: 29343500.38

Strategy                             SmaCross
MA1                                        20
MA2                                        50
Start                     2000-01-03 00:00:00
End                       2024-12-31 00:00:00
Duration                   9129 days 00:00:00
Equity Start [$]                    1000000.0
Equity Final [$]              29343500.384917
Equity Peak [$]               30253994.128875
Commissions [$]                           0.0
Cum Return [%]                        2834.35
Return (Ann.) [%]                     14.5018
Volatility (Ann.) [%]                 25.7613
CAGR [%]                                 9.78
Sharpe Ratio                           0.6617
Skew                                   -3.199
Kurtosis                             102.0707
Smart Sharpe Ratio                    -0.8039
Sortino Ratio                          0.9247
VWR Ratio                              4.8799
Calmar Ratio                           0.2017
Recovery factor [%]                    5.9154
Max. Drawdown [%]                    -71.9018
Avg. Drawdown [%]                     -4.7983
Max. Drawdown Duration     1666 days 00:00:00
Avg. Drawdown Duration       59 days 00:00:00
Drawdown Peak             2001-07-25 00:00:00
# Trades                                   66
Win Rate [%]                          56.0606
Best Trade [%]                       104.0816
Worst Trade [%]                      -63.5437
Avg. Trade [%]                         5.5053
Max. Trade Duration         276 days 00:00:00
Avg. Trade Duration          89 days 00:00:00
Profit Factor                          1.1704
Expectancy [%]                         0.0676
SQN                                    2.4064
Kelly Criterion [%]                   39.2016
dtype: object
end

It will create two HTML files and open it in your current browser.

• smacross.html - charts and trade stats

• smacross_stats.html - quantstats like strategy report

Features:

Live Trading and backtesting platform written in Python.

• Live Data Feed and Trading with

  • Interactive Brokers (needs IbPy and benefits greatly from an installed pytz)
  • Visual Chart (needs a fork of comtypes until a pull request is integrated in the release and benefits from pytz)
  • Oanda (needs oandapy) (REST API Only - v20 did not support streaming when implemented)

• Data feeds from csv/files, online sources or from pandas and blaze

• Filters for datas, like breaking a daily bar into chunks to simulate intraday or working with Renko bricks

• Multiple data feeds and multiple strategies supported

• Multiple timeframes at once

• Integrated Resampling and Replaying

• Step by Step backtesting or at once (except in the evaluation of the Strategy)

• Integrated battery of indicators

• TA-Lib indicator support (needs python ta-lib / check the docs)

• Easy development of custom indicators

• Analyzers (for example: TimeReturn, Sharpe Ratio, SQN) and pyfolio integration (deprecated)

• Flexible definition of commission schemes

• Integrated broker simulation with Market, Close, Limit, Stop, StopLimit, StopTrail, StopTrailLimitand OCO orders, bracket order, slippage, volume filling strategies and continuous cash adjustmet for future-like instruments

• Sizers for automated staking

• Cheat-on-Close and Cheat-on-Open modes

• Schedulers

• Trading Calendars

• Plotting (requires matplotlib)

Documentation

The old blog for backtrader:

• Blog <http://www.backtrader.com/blog>_

Read the full old documentation at:

• Documentation <http://www.backtrader.com/docu>_

List of built-in Indicators (122)

• Indicators Reference <http://www.backtrader.com/docu/indautoref.html>_

An example for IB Data Feeds/Trading:

• IbPy doesn't seem to be in PyPi. Do either::

  pip install git+https://github.com/blampe/IbPy.git

  or (if git is not available in your system)::

  pip install https://github.com/blampe/IbPy/archive/master.zip

For other functionalities like: Visual Chart, Oanda, TA-Lib, check the dependencies in the documentation.

From source:

• Place the backtrader_next directory found in the sources inside your project

Version numbering

X.Y.Z

• X: Major version number. Should stay stable unless something big is changed like an overhaul to use numpy
• Y: Minor version number. To be changed upon adding a complete new feature or (god forbids) an incompatible API change.
• Z: Revision version number. To be changed for documentation updates, small changes, small bug fixes