alpyen 0.1.3

Python based algo trading platform for IB.

alpyen

A lite-weight backtesting and live-trading algo engine for IB (Interactive Brokers) and other brokers.

• Free software: GNU General Public License v3

• Documentation: https://alpyen.readthedocs.io.

Features

Providing a trading platform for IB that includes the functions of

• Data gathering

• Algo signal calculation

• Automatic trading

• Book management

Current Version

Able to perform backtesting. Live trading support in development.

Next Release

Live trading support.

Support This Project

• Use and discuss us

• Report a bug

• Submit a bug fix

Installation

pip install alpyen

Example

from alpyen import datacontainer
from alpyen import backtesting

# Read data (assuming that BBH.csv from Yahoo Finance is in the Data folder)
data_folder_path = 'Data\\'
ticker_name = 'BBH'
short_lookback = 5
long_lookback = 200
ticker_names = [ticker_name]
all_input = datacontainer.DataUtils.aggregate_yahoo_data(ticker_names, data_folder_path)

# Subscribe to signals
signal_info_dict = {}
signal_info_dict[ticker_name + '_MA_' + str(short_lookback)]\
    = backtesting.SignalInfo(ticker_names, [short_lookback], 'MA')
signal_info_dict[ticker_name + '_MA_' + str(long_lookback)]\
    = backtesting.SignalInfo(ticker_names, [long_lookback], 'MA')

# Subscribe to strategies
strategy_info_dict = {}
strategy_name = ticker_name + '_MACrossing_01'
strategy_info_dict[strategy_name] = backtesting.StrategyInfo(
    [ticker_name + '_MA_' + str(short_lookback), ticker_name + '_MA_' + str(long_lookback)],
    [1], [ticker_name], 'MACrossing')

# Create backtester and run backtest
number_path = 1000
my_backtester = backtesting.Backtester(all_input, ticker_names, signal_info_dict, strategy_info_dict,
                                       number_path)
my_backtester.run_backtest()
backtest_results = my_backtester.get_results()

The moving average signal and the MA-crossing trading strategy are intended to serve as examples. Users can use them as references and create their custom signals/strategies by deriving from the SignalBase class within the signal module, and the StrategyBase class within the strategy module. Note that the package needs a unique signature string for each derived signals/strategies for reflective object creation, so for example:

class MASignal(SignalBase):
    """
    Moving average signal.
    """

    _signal_signature = 'MA'

class MACrossingStrategy(StrategyBase):
    """
    MA Crossing Strategy
    """

    _strategy_signature = 'MACrossing'

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2021-09-12)

• First release on PyPI.