investing-algorithm-framework 1.6.2

A framework for creating an investment algorithm

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Investing Algorithm Framework

The Investing Algorithm Framework is a Python tool that enables swift and elegant development of investment algorithms and trading bots. It comes with all the necessary components for creating algorithms, including data provisioning, portfolio management, and order execution.

Example implementation

The following algorithm connects to binance and buys BTC every 5 seconds. It also exposes an REST API that allows you to interact with the algorithm.

import pathlib
from datetime import datetime, timedelta

from investing_algorithm_framework import create_app, PortfolioConfiguration, \
    RESOURCE_DIRECTORY, TimeUnit, CCXTOHLCVMarketDataSource, Algorithm

def start_date_func():
    return datetime.utcnow() - timedelta(days=17)

# Define market data sources
bitvavo_btc_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(
    identifier="BTC",
    market="BITVAVO",
    symbol="BTC/EUR",
    timeframe="2h",
    start_date_func=start_date_func
)
app = create_app({RESOURCE_DIRECTORY: pathlib.Path(__file__).parent.resolve()})
app.add_portfolio_configuration(
    PortfolioConfiguration(
        market="BITVAVO",
        api_key="<your api key>",
        secret_key="<your secret key>",
        trading_symbol="USDT"
    )
)


@app.strategy(
    time_unit=TimeUnit.HOUR, 
    interval=2, 
    market_data_sources=[bitvavo_btc_eur_ohlcv_2h]
)
def perform_strategy(algorithm: Algorithm, market_data):
    print(
        f"Performing trading strategy on market " +
        f"data {market_data[bitvavo_btc_eur_ohlcv_2h.get_identifier()]}"
    )

if __name__ == "__main__":
    app.run()

You can find more examples here folder.

Backtesting

The framework also supports backtesting. You can use the same code as above, but instead of running the algorithm, you can run a backtest.

import pathlib
from datetime import datetime, timedelta

from investing_algorithm_framework import create_app, RESOURCE_DIRECTORY, \
    TimeUnit, CCXTOHLCVMarketDataSource, Algorithm

def start_date_func():
    return datetime.utcnow() - timedelta(days=17)

# Define market data sources
bitvavo_btc_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(
    identifier="BTC",
    market="BITVAVO",
    symbol="BTC/EUR",
    timeframe="2h",
    start_date_func=start_date_func
)
app = create_app({RESOURCE_DIRECTORY: pathlib.Path(__file__).parent.resolve()})

@app.strategy(
    time_unit=TimeUnit.HOUR, 
    interval=2, 
    market_data_sources=[bitvavo_btc_eur_ohlcv_2h]
)
def perform_strategy(algorithm: Algorithm, market_data):
    print(
        f"Performing trading strategy on market " +
        f"data {market_data[bitvavo_btc_eur_ohlcv_2h.get_identifier()]}"
    )

if __name__ == "__main__":
    backtest_report = app.backtest(
        start_date=datetime(2023, 11, 12) - timedelta(days=10),
        end_date=datetime(2023, 11, 12),
        unallocated=400,
        trading_symbol="EUR"
    )
    pretty_print_backtest(backtest_report)

For more examples, check out the examples folder.

Broker/Exchange configuration

The framework has by default support for ccxt. This should allow you to connect to a lot of brokers/exchanges.

from investing_algorithm_framework import App, PortfolioConfiguration
app = App()
app.add_portfolio_configuration(
    PortfolioConfiguration(
        market="BITVAVO", 
        api_key="xxxx", 
        secret_key="xxxx", 
        track_from="01/01/2022",
        trading_symbol="EUR"
    )
)

Download

You can download the framework with pypi.

pip install investing-algorithm-framework

Disclaimer

If you use this framework for your investments, do not risk money which you are afraid to lose, until you have clear understanding how the framework works. We can't stress this enough:

BEFORE YOU START USING MONEY WITH THE FRAMEWORK, MAKE SURE THAT YOU TESTED YOUR COMPONENTS THOROUGHLY. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR INVESTMENT RESULTS.

Also, make sure that you read the source code of any plugin you use or implementation of an algorithm made with this framework.

For further information regarding usage and licensing we recommend going to the licensing page at the website.

Documentation

All the documentation can be found online at the documentation webstie

In most cases, you'll probably never have to change code on this repo directly if you are building your algorithm/bot. But if you do, check out the contributing page at the website.

If you'd like to chat with investing-algorithm-framework users and developers, join us on Slack or join us on reddit

Acknowledgements

We want to thank all contributors to this project. A full list of all the people that contributed to the project can be found here

Bugs / Issues

If you discover a bug in the framework, please search our issue tracker first. If it hasn't been reported, please create a new issue.

Contributing

Feel like the framework is missing a feature? We welcome your pull requests! If you want to contribute to the project roadmap, please take a look at the project board. You can pick up a task by assigning yourself to it.

Note before starting any major new feature work, please open an issue describing what you are planning to do. This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.

Important: Always create your feature or hotfix against the develop branch, not master.