trading-server
A multi-asset, multi-strategy, event-driven trade execution and management platform for running many algorithms/bots at many venues simultaneously, with unified risk management and reporting.
This is not a standalone trading bot. You need to install and run this on a server or VPS using your own trading algorithms.
Installation
Using python 3.9
- Install mongodb (https://www.mongodb.com/)
- Install TA-Lib python bindings (links to wheels here https://blog.quantinsti.com/install-ta-lib-python/) and binaries (https://mrjbq7.github.io/ta-lib/install.html)
- Set up a telegram bot, record the bot key in enviroment variable TELEGRAM_BOT_TOKEN.
- Create a whitelist for telegram account ID's you want to have control of the server, recorded in environment variable TELEGRAM_BOT_WHITELIST, eg [<USER_ID_1>, <USER_ID_2>]
- Set up accounts for all venues you will trade at, recording API keys and secret keys in environment variables <VENUE_NAME>_API_KEY and <VENUE_NAME>_API_SECRET
- Configure what venues, instruments, models and timeframes you want to trade in server.py and model.py.
- Install dependencies in requirments.txt
- Run the server with python server_test.py. Note it will take some time to fetch historical data for the instruments you are trading.
Current features
Trade any API-accessible market with unified multi-strategy portfolio management, autonomously or semi-autonomously.
Allocation-based risk management (allocate x% of capital to specific strategies with y% exposure per strategy).
Porfolio performance metrics and tracking. Tracks the following:
Feature library - assemble new strategies quickly from existing features.
Trade consent via Telegram (or write your own messaging client). Accept, veto or tweak trade setups before they are actioned.
WIP features
Account multicasting - trade as many accounts on as many platforms as desired.
UI - web dashboard for portfolio stats and individual trade metrics
Integration with Backtrader
Blockchain-based strategy auditing - publish trade signals to IPFS and Ethereum/BSC to empirically prove win rate over time
Accounting and compliance reporting
Venue support
| Venue | Integration status | Instrument types |
|---|---|---|
 |
Complete | Crypto derivatives |
 |
In progress | Crypto spot, options & derivatives |
 |
Planned | Crypto spot & derivatives |
| IG Markets | Planned | FX, equity, commodity & index CFD's |
| Interactive Brokers | Planned | FX, equity, commodity & index CFD's |
| Deribit | Planned | Crypto derivatives & options |
Market data
1 minute resolution OHLCV bars for all watched instruments are stored with MongoDB.
This software works with 1 minute and above resolution strategies. Tick-resolution support planned later. With this in mind, the software converts tick data to 1 min bars where live tick data is available, but doesn't store ticks locally (i.e. it handles tick data but doesnt use it as is, yet).
Strategy modellling
Individual strategy implementations are not included. A simple moving average cross model is included as an example only. Custom strategy implementations, collaboration or any other enquiries please email me at sam@sdbgroup.io.
Collaboration
Feature requests and discussion regarding new features are very welcome, please reach out.
External libraries
TA-LIB - https://mrjbq7.github.io/ta-lib/
Backtrader - https://www.backtrader.com/
Acknowledgements
Based on architecture described by Michael Halls-Moore at QuantStart.com (qsTrader), and written works by E. Chan and M. Lopez de Prado. Thanks all.
License
GNU GPLv3