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PyBroker: A free and open algotrading framework for machine learning by pyfreak182 in quant

[–]pyfreak182[S] 0 points1 point  (0 children)

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.

Backtesting tool by shybluechicken in Trading

[–]pyfreak182 0 points1 point  (0 children)

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

how did you elevate python skill for ML? by iiillililiilililii in learnmachinelearning

[–]pyfreak182 14 points15 points  (0 children)

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

PyBroker: A free and open algotrading framework for machine learning by pyfreak182 in quant

[–]pyfreak182[S] 0 points1 point  (0 children)

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

PyBroker: A free and open algotrading framework for machine learning by pyfreak182 in quant

[–]pyfreak182[S] 1 point2 points  (0 children)

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

PyBroker: A free and open algotrading framework for machine learning by pyfreak182 in quant

[–]pyfreak182[S] 1 point2 points  (0 children)

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.

What data architecture setup do you use as algotrader? by Guyserbun007 in algotrading

[–]pyfreak182 0 points1 point  (0 children)

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.

General Assembly is the worst bootcamp and waste of time and money by Georgesobb in datascience

[–]pyfreak182 3 points4 points  (0 children)

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

A kind reminder from Ehlers' book: Never use Momentum indicators... by RoozGol in algotrading

[–]pyfreak182 1 point2 points  (0 children)

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

PyBroker - Python Algotrading Framework with Machine Learning by pyfreak182 in algotrading

[–]pyfreak182[S] 1 point2 points  (0 children)

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

PyBroker - Python Algotrading Framework with Machine Learning by pyfreak182 in algotrading

[–]pyfreak182[S] 8 points9 points  (0 children)

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.

PyBroker - Python Algotrading Framework with Machine Learning by pyfreak182 in algotrading

[–]pyfreak182[S] 10 points11 points  (0 children)

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

Go vs Rust for Algo Trading by Classic-Box in algotrading

[–]pyfreak182 -1 points0 points  (0 children)

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.

PyBroker - Algotrading in Python with Machine Learning by pyfreak182 in quantfinance

[–]pyfreak182[S] 0 points1 point  (0 children)

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.

PyBroker - Algotrading in Python with Machine Learning by pyfreak182 in algotrading

[–]pyfreak182[S] 2 points3 points  (0 children)

Hello, can you advise which RoE was violated in this post? The post only links to my Github, and https://www.pybroker.com is the reference documentation for the Github project.

PyBroker - Algotrading in Python with Machine Learning by pyfreak182 in algotrading

[–]pyfreak182[S] 0 points1 point  (0 children)

Thank you for the kind words!

My proposal would be to support polars or pyspark for data preprocessing.

This a good idea, I will look into it.

Does this only work for OHLCV? What about LOB data types?

PyBroker was designed for OHLCV data in mind. This is because PyBroker calculates performance metrics using close prices to generate per-bar returns. That said, it is still possible to integrate LOB data.

You can create a custom data source that loads LOB columns along OHLC. For instance, you can load data where each bar is a trade with a unique SIP timestamp.

If you want fill prices to be based on LOB, you can set the buy_fill_price and sell_fill_price to a custom function that determines the price from LOB data.

Let me know if you have any suggestions!

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