awesome-quant

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.

The S&P500 is a backtesting reference benchmark. You can compare portfolio performance through drawdown periods with backtesting tools. Macroeconomic drawdowns are good times to have cash for acquisitions; instead of free government cheese. "Tear Sheets: Definition and Examples in Finance, Vs. Prospectus" https://www.investopedia.com/terms/t/tearsheets.asp : > While tear sheets date back to the old days when stockbrokers would rip individual pages out of the S&P summary book and send them to current or potential clients, most information is extracted online today. Therefore, any concise representation of a company's business fundamentals could be considered a tear sheet. From https://news.ycombinator.com/item?id=24428206#24429801 : > pyfolio.tears.create_interesting_times_tear_sheet measures algorithmic trading algorithm performance duri

/r/quant /r/algotrading 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

Is it possible to factor (e.g. GRI) sustainability criteria into the portfolio fitness function? https://news.ycombinator.com/item?id=21922558 My concern is that - like any other portfolio optimization algorithm - blindly optimizing on fundamentals and short term returns will lead to investing in firms who just dump external costs onto people in the present and future; so, screening with sustainability criteria is important to me. From https://news.ycombinator.com/item?id=19111911 : > awesome-quant lists a bunch of other tools for algos and superalgos: https://github.com/wilsonfreitas/awesome-quant

Is there something like that, but with more predictable languages like Rust[1], or OCaml? I wouldn't trust my money for dynamic typing. I recently asked the same question[2] to the Futu broker[3]. Sadly no answer. I wish more quant companies would pay attention to the predictability of their own code and external APIs. For example using Imandra[4] to reason about their own code. Using their own protocol language specification[5] they created verified FIX library[6]. P.S. Also you might want to add that framework in Awesome Quant list[7]. [1] https://internals.rust-lang.org/t/proposal-business-applicat... [2] https://github.com/FutunnOpen/futu-api-doc/issues/2 [3] https://www.futu5.com/en/ [4] https://imandra.ai/ [5] https://docs.imandra.ai/ipl/ [6] https://docs.imandra.ai/fix-engine/ [7] https://github.com/wilsonfreitas/awesome-quant

> How well does it do in production though and what happens when multiple algos execute the same trades? Price inflation. > Does it cause the rest of the algos to adapt and change results? Trading index ETFs? IDK > It makes sense to back-test together and work on it, but if it's proven to work, someone will create something to monitor volume on those trades and work against it. Why does it need to do lots of trades? Is it possible for anyone other than e.g. SEC to review trades by buyer or seller? > I'd be curious to see the same algo do 300% in production, and if so, then my bias would be uncalled for. pyfolio does tear sheets with Zipline algos: pyfolio/examples/zipline_algo_example.ipynb https://nbviewer.jupyter.org/github/quantopian/pyfolio/blob/... alphalens does performance analysis of predictive factors: alphalens/examples/pyfolio_