alpyen
A lite-weight backtesting and live-trading algo engine for multiple brokers:
- Interactive Brokers (IB)
- Gemini
License: 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 monitoring and portfolio management
Current Version
Able to perform backtesting and live trading.
Support This Project
- Use and discuss us
- Report a bug
- Submit a bug fix
Installation
pip install alpyen
"Hello World"/Quick Start
For a quick demo, do the following:
- Install alpyen
- Create a py file that perform either backtesting (use the test_backtesting_macrossing_reshuffle test as an example) or live trading (use the test_live_trading test as an example)
- For live trading, create a yml control file (use the test_control.yml file as an example)
Example
from alpyen import datacontainer
from alpyen import backtesting
from alpyen import utils
# Read data (assuming that BBH.csv from Yahoo Finance is in the Data folder)
data_folder = 'Data\\'
ticker_name = 'BBH'
file_path = os.path.join(os.path.dirname(__file__), data_folder)
short_lookback = 5
long_lookback = 200
short_lookback_name = ticker_name + '_MA_' + str(short_lookback)
long_lookback_name = ticker_name + '_MA_' + str(long_lookback)
ticker_names = [ticker_name]
all_input = datacontainer.DataUtils.aggregate_yahoo_data(ticker_names, file_path)
# Subscribe to signals
signal_info_dict = {}
signal_info_dict[short_lookback_name]\
= utils.SignalInfo('MA', ticker_names, [], [], short_lookback, {})
signal_info_dict[long_lookback_name]\
= utils.SignalInfo('MA', ticker_names, [], [], long_lookback, {})
# Subscribe to strategies
strategy_info_dict = {}
strategy_name = ticker_name + '_MACrossing_01'
strategy_info_dict[strategy_name] = utils.StrategyInfo(
'MACrossing',
[short_lookback_name, long_lookback_name],
1, {}, ticker_names, combo_definition={'combo1': [1.0]})
# 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 / MA-crossing trading strategy; and
- weighted momentum signal / VAA strategy
are built-in in the package, and 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.
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