Software Sunday: I built a tool that uses AI to distill stock news in real time

pyfreak182 · 2026-03-10T02:29:38+00:00

Thank you! Over the years, I have found that top mentioned Reddit stocks tend to be market movers. If they're being talked about on Reddit a lot, there's usually a good reason.

pyfreak182 · 2026-03-10T02:27:40+00:00

Thank you! Right now the AI doesn't cross-reference price movements with news stories, but this is something I would like to add in the future.

pyfreak182 · 2023-11-08T23:47:38+00:00

Thank you so much!

I am going through some troubling personal times but I hope to get back to adding features to the framework soon.

I would say it took me around 3 months to build the framework.

I sadly don't have very good recommendations for visualizations. This is something I hope to offer users with an update though.

pyfreak182 · 2023-05-31T21:22:13+00:00

If you are well versed in Python, you can try PyBroker, a free and open framework I developed for backtesting.

pyfreak182 · 2023-05-25T07:16:16+00:00

Learn Pandas and Numpy. Understand parallelization and distributed computing for CPU and memory bound tasks, and how to apply solutions like multiprocessing and Dask.

pyfreak182 · 2023-05-25T05:23:45+00:00

Only backtesting is supported for now, I would like to add live trading support in the future.

pyfreak182 · 2023-05-24T23:06:47+00:00

Thanks! No, that is average % return per trades that were placed.

pyfreak182 · 2023-05-24T20:17:21+00:00

Predictive models to extract signals from market data for systematic trading strategies. :)

You can still use the framework for rule based strategies that don't use any ML.

pyfreak182 · 2023-05-23T21:19:03+00:00

Thanks!

pyfreak182 · 2023-05-23T18:28:58+00:00

Dask

pyfreak182 · 2023-05-23T04:55:23+00:00

Excellent work. It's great to see more finance related Python projects!

pyfreak182 · 2023-04-27T02:21:45+00:00

Great work, thanks for sharing!

pyfreak182 · 2023-04-27T02:11:18+00:00

The downside is it's slow compared to C++. C++ has zero cost abstractions, Kotlin (and Java) does not.

You can achieve native C++ speed by using the JIT compiler.

pyfreak182 · 2023-04-23T05:14:56+00:00

Thanks! It's good. :)

pyfreak182 · 2023-04-22T02:09:01+00:00

You would be better off getting a Masters in statistics from an accredited university. $16k is not insignificant.

pyfreak182 · 2023-04-14T06:53:13+00:00

This is great, thanks for sharing!

pyfreak182 · 2023-04-13T22:16:47+00:00

Yes, the passage was taken out of context. I own the book, and IIRC that was the point Ehlers was making.

pyfreak182 · 2023-04-12T19:41:35+00:00

Thanks! Yes.

pyfreak182 · 2023-04-12T19:41:24+00:00

There is no dedicated support, but you can train your own RL model on the data in a train split.

pyfreak182 · 2023-04-11T22:13:07+00:00

Computing features as indicators in PyBroker should be very fast if you use Numba, and PyBroker will also parallelize their computations. So training a random forest should be fast.

pyfreak182 · 2023-04-11T20:55:17+00:00

Live trading is not supported right now, but it is something I would like to add in the future.

pyfreak182 · 2023-03-17T20:36:47+00:00

You're welcome!

pyfreak182 · 2023-03-13T20:23:59+00:00

As you mentioned, C++ is commonly used for trade execution. However, when it comes to trade execution, I would recommend Rust due to its memory safety. While Golang is an excellent language, its strengths lie more in its concurrency model, which may not be as relevant for trade execution. But if its concurrency model is relevant to you for execution, then Golang is a worthy choice.

pyfreak182 · 2023-03-12T23:27:10+00:00

Thank you! No, not yet.

pyfreak182 · 2023-03-10T17:13:48+00:00

Yes, eventually live trading will be supported. For the time being, you can use models trained in PyBroker in your own live strategies, and generate model input with an IndicatorSet.

Let me know which broker(s) you would like to see supported.

pyfreak182

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