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Wolfinch

Wolfinch is a trading bot implemented in Python. It supports algorithmic trading for equity market and cryptocurrency exchanges. The modularized implementation is easily extensible to support more exchanges, trading strategies and indicators. A simple UI is available out of the box to view trades and allow basic controls in operation.

Using Wolfinch

Wolfinch could be run in a local computer or in a VPS as a native python process or as a docker container.

Running natively

Starting the bot is fairly straight forward. Install the required packages and run one of the following command based on the intended mode.

A few examples below covers most startup modes:

• Fresh start: wolfinch.py --config <config.yml>
• Import historic data and exit: wolfinch.py --config <config.yml> --import_only
• Backtesting mode: wolfinch.py --config <config.yml> --backtesting
• Restart from previous state: wolfinch.py --config <config.yml> --restart
• Restart in Genetic Optimizer mode: wolfinch.py --config <config.yml> --ga_restart

Lots of sample config files are available in config/ directory. Those should serve as a very good starting point.

Running as a Docker container

Pull docker image from the dockerhub and run using the following command. Two volume mounts are required, one for the config files and other as data directory. The default UI port is 8080. In case a custom port is configured, it has to be exposed while running the container.

docker run -v </PATH/config>:/config -v </PATH/data>:/data -p 8080:8080 wolfinch:1.2.0 --config config/wolfinch_config.yml [optional-params]

Features available :

• Auto / Manual Trading Modes
• Multiple decision engines
• YAML based rich configuration file support - Tons of nuts and bolts to customize
• Out of the box UI availability
• Pluggable Strategies
• Pluggable Indicators
• Pluggable Exchanges
• Backtesting support
• Paper trading (Simulation) mode
• Genetic optimizer support for tuning strategies
• Machine Learning mode - supports training and using trained models and ML decision engines
• Positional Stop Stop Loss support, multiple smart stop strategies available
  • Fixed Percent
  • Trailing with fixed percent
  • ATR trailing stop (with variable ATR period support)
  • Strategy provided
• Positional Take profit support
  • Fixed percent
  • Strategy provided
• Supports trading multiple exchanges at the same time
• Supports trading multiple trading pairs at the same time
• Supports cross exchange, cross pair trading (Using signals and indicators on one exchange/trading-pair to make trading decision on another exchange/trading-pair)
• Restartability (of live trading, backtesting, genetic optimizer)

Read More here:

Introduction-to-the-friendly-trading-bot

algorithmic-trading-with-robinhood-using-wolfinch

algorithmic-trading-with-binance-using-wolfinch

Join subreddit - wolfinch

Supported Exchanges

• Coinbase Pro
• Binance
• Binance US
• Robinhood
• gdax [deprecated]

Further Enhancements:

Not based on priority.

1. more indicators
2. more strategies
3. improve Decision/Model
4. more exchanges
5. integrate news source, sentiment analysis input for decision
6. Any feature requests

NOTE:

Read third-party Readme for dependencies

Donate:

You can donate to appreciate the countless hours spent on the development.

• BTC : 35bYjx9Geo6gLM41nqRnZA5KpciJEfJokD
• ETH : 0x2598eA883719a679deEf821736fa39DF0DD9F86C
• LTC : MRfdbKHUrSxv2zKztdVyodKwSzpQNgofr8

Disclaimer:

Project is for research and educational purposes only. No data or code provided are financial advice. Trading strategies are experimental and provided as is. Use at your own risk.

License

GNU General Public License v3.0 or later

See LICENSE to see the full text.