investing-algorithm-framework

Release 3.6.0

A framework for creating trading bots

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Keywords
algorithmic-trading, backtesting, backtesting-trading-strategies, cryptocurrency, python, trade, trading, trading-bot, trading-bots, trading-strategies
License
Apache-2.0
Install
pip install investing-algorithm-framework==3.6.0

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Investing Algorithm Framework

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

Features:

  • Order execution
  • Broker and exchange connections through ccxt
  • Backtesting and performance analysis reports example
  • Backtest experiments to optimize your trading strategy example
  • Portfolio management
  • Web API for interacting with your deployed trading bot
  • Data persistence through sqlite db or an in-memory db
  • Stateless running for cloud function deployments
  • Polars dataframes support out of the box for fast data processing pola.rs

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 investing_algorithm_framework import create_app, PortfolioConfiguration, \
    RESOURCE_DIRECTORY, TimeUnit, CCXTOHLCVMarketDataSource, Algorithm, \
    CCXTTickerMarketDataSource, MarketCredential, SYMBOLS

# Define the symbols you want to trade for optimization, otherwise the 
# algorithm will check if you have orders and balances on all available 
# symbols on the market
symbols = ["BTC/EUR"]

# Define resource directory and the symbols you want to trade
config = {
    RESOURCE_DIRECTORY: pathlib.Path(__file__).parent.resolve()
    SYMBOLS: symbols
}

# Define market data sources
# OHLCV data for candles
bitvavo_btc_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(
    identifier="BTC-ohlcv",
    market="BITVAVO",
    symbol="BTC/EUR",
    timeframe="2h",
    window_size=200
)
# Ticker data for orders, trades and positions
bitvavo_btc_eur_ticker = CCXTTickerMarketDataSource(
    identifier="BTC-ticker",
    market="BITVAVO",
    symbol="BTC/EUR",
)
app = create_app(config=config)
algorithm = Algorithm()

app.add_market_data_source(bitvavo_btc_eur_ohlcv_2h)
app.add_market_data_source(bitvavo_btc_eur_ticker)
app.add_market_credential(MarketCredential(
    market="bitvavo",
    api_key="<your api key>",
    secret_key="<your secret key>",
))
app.add_portfolio_configuration(
    PortfolioConfiguration(
        market="bitvavo",
        trading_symbol="EUR",
        initial_balance=400
    )
)
app.add_algorithm(algorithm)

@algorithm.strategy(
    # Run every two hours
    time_unit=TimeUnit.HOUR, 
    interval=2, 
    # Specify market data sources that need to be passed to the strategy
    market_data_sources=["BTC-ticker", "BTC-ohlcv"]
)
def perform_strategy(algorithm: Algorithm, market_data: dict):
    # By default, ohlcv data is passed as polars df in the form of
    # {"<identifier>": <dataframe>}  https://pola.rs/, 
    # call to_pandas() to convert to pandas
    polars_df = market_data["BTC-ohlcv"]  
    print(f"I have access to {len(polars_df)} candles of ohlcv data")

    # Ticker data is passed as {"<identifier>": <ticker dict>}
    ticker_data = market_data["BTC-ticker"]
    unallocated_balance = algorithm.get_unallocated()
    positions = algorithm.get_positions()
    trades = algorithm.get_trades()
    open_trades = algorithm.get_open_trades()
    closed_trades = algorithm.get_closed_trades()
    
    # Create a buy oder 
    algorithm.create_limit_order(
        target_symbol="BTC/EUR",
        order_side="buy",
        amount=0.01,
        price=ticker_data["ask"],
    )
    
    # Close a trade
    algorithm.close_trade(trades[0].id)
    
    # Close a position
    algorithm.close_position(positions[0].get_symbol())
    
if __name__ == "__main__":
    app.run()

You can find more examples here folder.

Backtesting and experiments

The framework also supports backtesting and performing backtest experiments. After a backtest, you can print a report that shows the performance of your trading bot.

To run a single backtest you can use the example code that can be found here.

Backtesting report

You can use the pretty_print_backtest function to print a backtest report. For example if you run the moving average example trading bot you will get the following backtesting report:

                          /&#                                      #&(                 Backtest report
                          &&&&&&&&&&&#                       &&&&&&&&&&&&              ---------------------------
                         &&&&&&&&&&&&&&&&                (&&&&&&&&&&&&&&&              Start date: 2023-08-24 00:00:00
                         &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&              End date: 2023-12-02 00:00:00
                         &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&              Number of days: 100 
                          &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&              Number of runs: 1201
                          .&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&               Number of orders: 10
                           &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&.               Initial balance: 400.0
                          &&&&&&&#  /(((   &&&&&&&&&&&&*(((     .&&&&&&&.              Final balance: 431.1499      
              &&&&&&&&&&&&&&&&&&&    ((((    &&&&&&&&   ((((     &&&&&&&&&&&&&&&&&&&   Total net gain: 28.5542 7.139%  
                       (((&&&&&&&&    ((((    &&&&&&     ((((   &&&&&&&&&((            Growth: 31.1499 7.787%             
               /((((((((((&&&&&&&&&&   (((,   &&&&&&      (((**&&&&&&&&&(((((((((((    Number of trades closed: 4
                     &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&          Number of trades open(end of backtest): 2
                            &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&                 Percentage positive trades: 60.0%
                              &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&                   Percentage negative trades: 20.0%
                     (((((      &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&(                      Average trade size: 98.9886 EUR
                    (((((           &&&&&&&&&&&&&&&&&&&&&&&&,                          Average trade duration: 184.0 hours
                   (((((                 &&&&&&&&&&&&&#                            
                  (((((                  #&&&&&&&&&&###                            
                 (((((                   &&&&&&&&&&&###.                           
               .(((((                   &&&&&&&&&&&&&&###(                          
              (((((                   &&&&&&&&&&&&&&&&#(((/                        
             (((((                  &&&&&&&&&&&&&&&&&&&@((((                       
            (((((                  &&&&&&&&&&&&&&&&&&&&&&((((                      
           (((((                  &&&&&&&&&&&&&&&&&&&&&&&&((((,                    
         .(((((                   &&&&&&&&&&&&&&&&&&&&&&&&&&((((                    
        (((((                   &&&&&&&&&&&&&&&&&&&&&&&&&&&&((((                   
       (((((                    &&&&&&&&&&&&&&&&&&&&&&&&&&&&&((((                  
      (((((                    &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&((((                  
     (((((                     &&&&&&&&&&&&&&&&&&&&&&&&&&&&&#((((                  
     (((((((((((((((((((#########&&&&&&&&&&&&&&&&&&&&&&&&&&&&(((((                  
      (((((((((((((((((((((######&&&&&&&&&&&&&&&&&&&&&&&&&&&&((((                   
        
Positions overview
╭────────────┬──────────┬──────────────────────┬───────────────────────┬──────────────┬───────────────┬───────────────────────────┬────────────────┬───────────────╮
│ Position   │   Amount │   Pending buy amount │   Pending sell amount │   Cost (EUR) │   Value (EUR) │ Percentage of portfolio   │   Growth (EUR) │ Growth_rate   │
├────────────┼──────────┼──────────────────────┼───────────────────────┼──────────────┼───────────────┼───────────────────────────┼────────────────┼───────────────┤
│ EUR        │ 217.044  │                    0 │                     0 │      217.044 │       217.044 │ 50.3407%                  │         0      │ 0.0000%       │
├────────────┼──────────┼──────────────────────┼───────────────────────┼──────────────┼───────────────┼───────────────────────────┼────────────────┼───────────────┤
│ BTC        │   0.003  │                    0 │                     0 │      104.372 │       106.84  │ 24.7802%                  │         2.4678 │ 2.3644%       │
├────────────┼──────────┼──────────────────────┼───────────────────────┼──────────────┼───────────────┼───────────────────────────┼────────────────┼───────────────┤
│ DOT        │  21.3295 │                    0 │                     0 │      107.138 │       107.266 │ 24.8791%                  │         0.128  │ 0.1195%       │
╰────────────┴──────────┴──────────────────────┴───────────────────────┴──────────────┴───────────────┴───────────────────────────┴────────────────┴───────────────╯
Trades overview
╭─────────┬─────────────────────┬─────────────────────┬────────────────────┬──────────────┬──────────────────┬───────────────────────┬────────────────────┬─────────────────────╮
│ Pair    │ Open date           │ Close date          │   Duration (hours) │   Size (EUR) │   Net gain (EUR) │ Net gain percentage   │   Open price (EUR) │   Close price (EUR) │
├─────────┼─────────────────────┼─────────────────────┼────────────────────┼──────────────┼──────────────────┼───────────────────────┼────────────────────┼─────────────────────┤
│ DOT-EUR │ 2023-11-30 18:00:00 │                     │            3207.26 │     107.138  │           0      │ 0.0000%               │             5.023  │                     │
├─────────┼─────────────────────┼─────────────────────┼────────────────────┼──────────────┼──────────────────┼───────────────────────┼────────────────────┼─────────────────────┤
│ BTC-EUR │ 2023-11-29 12:00:00 │                     │            3237.26 │     104.372  │           0      │ 0.0000%               │         34790.7    │                     │
├─────────┼─────────────────────┼─────────────────────┼────────────────────┼──────────────┼──────────────────┼───────────────────────┼────────────────────┼─────────────────────┤
│ BTC-EUR │ 2023-11-07 22:00:00 │ 2023-11-14 14:00:00 │             160    │      99.2337 │           2.5395 │ 2.5591%               │         33077.9    │          33924.4    │
├─────────┼─────────────────────┼─────────────────────┼────────────────────┼──────────────┼──────────────────┼───────────────────────┼────────────────────┼─────────────────────┤
│ BTC-EUR │ 2023-11-06 14:00:00 │ 2023-11-06 18:00:00 │               4    │      98.2854 │          -0.4248 │ -0.4322%              │         32761.8    │          32620.2    │
├─────────┼─────────────────────┼─────────────────────┼────────────────────┼──────────────┼──────────────────┼───────────────────────┼────────────────────┼─────────────────────┤
│ DOT-EUR │ 2023-10-30 04:00:00 │ 2023-11-14 00:00:00 │             356    │     100.537  │          24.2886 │ 24.1588%              │             4.0565 │              5.0365 │
├─────────┼─────────────────────┼─────────────────────┼────────────────────┼──────────────┼──────────────────┼───────────────────────┼────────────────────┼─────────────────────┤
│ BTC-EUR │ 2023-09-13 14:00:00 │ 2023-09-22 14:00:00 │             216    │      97.8976 │           2.1508 │ 2.1970%               │         24474.4    │          25012.1    │
╰─────────┴─────────────────────┴─────────────────────┴────────────────────┴──────────────┴──────────────────┴───────────────────────┴────────────────────┴─────────────────────╯

Backtest experiments

The framework also supports backtest experiments. Backtest experiments allows you to compare multiple algorithms and evaluate their performance. Ideally, you would do this by parameterizing your strategy and creating a factory function that creates the algorithm with the different parameters. You can find an example of this in the backtest experiments example.

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 PortfolioConfiguration, \
    MarketCredential, create_app
app = create_app()
app.add_market_credential(
    MarketCredential(
        market="<your market>", 
        api_key="<your api key>",
        secret_key="<your secret key>",
    )
)
app.add_portfolio_configuration(
    PortfolioConfiguration(
        market="<your market>", 
        initial_balance=400,
        track_from="01/01/2022",
        trading_symbol="EUR"
    )
)

Performance

We are continuously working on improving the performance of the framework. If you have any suggestions, please let us know.

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.

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

The investing algorithm framework is a community driven project. We welcome you to participate, contribute and together help build the future trading bots developed in python.

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.

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