bt VS awesome-quant

bt – bt is a flexible backtesting framework for Python used to test quantitative trading strategies.

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A better way would be to create a master strategy that manages 100 other strategies, each with $10 a pop. The bt backtesting framework was designed to support this. That way you can manage your portfolio from one account, but also use multiple strategies. The master strategy just handles fund allocation, and rebalancing if necessary.

No, it looks more like a Rust equivalent of libraries like ffn (financial functions for python) or many of the other ones listed here https://github.com/wilsonfreitas/awesome-quant

Using rust to do exploratory analysis in python seems like a misguided idea. But using rust to productize models that have performance and accuracy sensitivities, the things that C/C++ is still used for, indeed sounds like a good idea.

Most of the python libraries used in finance, like numpy/pandas, call out to C for performance reasons; the libraries are essentially python bindings + syntax to C functions. It would be interesting to think about replacing that backend with rust.

This is a good list https://github.com/wilsonfreitas/awesome-quant

also, what are the best quanty python packages that you like to see an applicant use? there are so many. https://github.com/wilsonfreitas/awesome-quant

If the financial analyst does not have a (possibly piecewise) software function to at least test with backtesting and paper trading, do they even have an objective relative performance statistic? Your notebook or better should also model fees and have a parametrizable initial balance.

Here's the awesome-quant link directory: https://github.com/wilsonfreitas/awesome-quant

A curated list of libraries, packages and resources for Quants\ (0 comments)