GoIB
quokka
| GoIB | quokka | |
|---|---|---|
| 1 | 23 | |
| 384 | 1,082 | |
| -0.3% | - | |
| 0.0 | 8.3 | |
| over 2 years ago | 7 months ago | |
| Go | Python | |
| - | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
GoIB
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Algorithmic Trading with Go
Yeah, I ended up taking https://github.com/gofinance/ib and wrote my own wrapper. This took a long time but has been stable since. I'm basically only doing buy lmt, sell lmt, cancel, and updates orders though. So, the logic is pretty simple. Catching all the return messages and structuring them correctly took tons of debugging, trail, and error. Basically, mapping the messages into the correct orders.
quokka
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How Query Engines Work
An awesome read!
Something related that I found out about from HN a few months back is another engine called quokka. It's particularly interesting and applicable how quokka schedules distributed queries to outperform Spark https://github.com/marsupialtail/quokka/blob/master/blog/why...
- Quokka – Distributed Polars on Ray
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Algorithmic Trading with Go
Hi Justin, you might be interested in my blog: https://github.com/marsupialtail/quokka/blob/master/blog/bac... advocating a cloud based approach.
You don't have to use the system I am building, but it's worth thinking about that design.
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Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
SQL support is very challenging.
I work on Quokka (https://github.com/marsupialtail/quokka). I support Iceberg reads. Recently we are adding SQL support from just parsing the DuckDB logical plan, though that is very challenging as well.
The Python world lacks a standard for a plug and play SQL query optimizer. Apache Calcite is good for the JVM world, but not great if you are trying to cut out the JVM.
- Why your dataframe library needs to understand vector embeddings
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The Inner Workings of Distributed Databases
In case people are interested, I wrote a post about fault tolerance strategies of data systems like Spark and Flink: https://github.com/marsupialtail/quokka/blob/master/blog/fau...
The key difference here is that these systems don't store data, so fault tolerance means recovering within a query instead of not losing data.
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Launch HN: DAGWorks – ML platform for data science teams
would love to collaborate on an integration with pyquokka (https://github.com/marsupialtail/quokka) once I put out a stable release end of this month :-)
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is spark always your go to solution ?
Then you should keep an eye on quokka. This may become the "Spark" for Polars/DuckDB. It seems to be under active development though I'm not sure how stable it is.
- Distributed fault tolerance made simple
- Fault tolerance for distributed data systems is quite simple
What are some alternatives?
gobacktest - event-driven backtesting framework written in golang
opteryx - 🦖 A SQL-on-everything Query Engine you can execute over multiple databases and file formats. Query your data, where it lives.
banking - Banking library written in Go.
cempaka - "Write a trading bot which buys low and sells high." Sounds simple enough, right?
accounting - money and currency formatting for golang
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
ofxgo - Golang library for querying and parsing OFX
spyql - Query data on the command line with SQL-like SELECTs powered by Python expressions
go-finance - :warning: Deprecrated in favor of https://github.com/piquette/finance-go
pg8000 - A Pure-Python PostgreSQL Driver
Golang Crypto Trading Bot - A golang implementation of a console-based trading bot for cryptocurrency exchanges
blog - Some notes on things I find interesting and important.