https://github.com/ivan-vasilev/atpy
Event-driven Algorithmic Trading For Python
https://github.com/ivan-vasilev/atpy
algorithmic-trading event-driven machine-learning python
Last synced: 9 months ago
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Event-driven Algorithmic Trading For Python
- Host: GitHub
- URL: https://github.com/ivan-vasilev/atpy
- Owner: ivan-vasilev
- License: mit
- Created: 2016-12-08T22:26:41.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2019-04-07T22:16:22.000Z (about 7 years ago)
- Last Synced: 2025-07-10T07:59:52.221Z (12 months ago)
- Topics: algorithmic-trading, event-driven, machine-learning, python
- Language: Python
- Homepage:
- Size: 639 KB
- Stars: 25
- Watchers: 7
- Forks: 9
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Event-based Algorithmic Trading For Python
Event-based Algorithmic trading library. The events implementation is [pyevents](https://github.com/ivan-vasilev/pyevents)
library. The features are:
* Real-time and historical bar and tick data from [IQFeed](http://www.iqfeed.net/) via [@pyiqfeed](https://github.com/akapur/pyiqfeed). The data is provided as pandas multiindex dataframes. For this to work, you need IQFeed subscription.
* API integration with [Quandl](https://www.quandl.com/) and [INTRINIO](https://intrinio.com/).
* Storing and retrieving historical data and other datasets with [PostgreSQL](https://www.postgresql.org) and [InfluxDB](https://www.influxdata.com/). Again, the data is provided via pandas dataframes.
* Placing orders via the [Interactive Brokers Python API](https://github.com/InteractiveBrokers/tws-api-public). For this to work, you need to have IB account.
For more information on how to use the library please check the unit tests.