awesome-ai-in-finance
awesome-quant
| awesome-ai-in-finance | awesome-quant | |
|---|---|---|
| 101 | 18 | |
| 2,887 | 16,153 | |
| - | - | |
| 5.2 | 8.8 | |
| about 1 month ago | 5 days ago | |
| Python | ||
| Creative Commons Zero v1.0 Universal | - |
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awesome-ai-in-finance
- awesome-ai-in-finance: curated list of books/online courses/papers on AI and finance. Topics include crypto trading strategies/ta/backter etc. Other Models - star count:2499.0
- awesome-ai-in-finance: curated list of books/online courses/papers on AI and finance. Topics include crypto trading strategies/ta/backter etc. Other Models - star count:2453.0
awesome-quant
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RustQuant: A Library for Quantitative Finance
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.
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Open Source Projects
This is a good list https://github.com/wilsonfreitas/awesome-quant
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I’m not a Quant, but a Headhunter - ask me anything
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
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Why building profitable trading bot is hard?
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
- For Traders Who Want To Be Quants
- A curated list of libraries, packages and resources for Quants
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Hacker News top posts: Feb 22, 2022
A curated list of libraries, packages and resources for Quants\ (0 comments)
What are some alternatives?
awesome-deep-learning - A curated list of awesome Deep Learning tutorials, projects and communities.
qlib - Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
awesome-jax - JAX - A curated list of resources https://github.com/google/jax
backtrader - Python Backtesting library for trading strategies
Machine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources
uniswap-sushiswap-arbitrage-bot - Two bots written in JS that uses flashswaps and normal swaps to arbitrage Uniswap. Includes an automated demostration.
downloads - This repository is used to hold convenient direct-link downloads.
CUDA.jl - CUDA programming in Julia.
Awesome-pytorch-list - A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
awesome-discord-communities - A curated list of awesome Discord communities for programmers
awesome-decision-transformer - A curated list of Decision Transformer resources (continually updated)
Gekko-Strategies - Strategies to Gekko trading bot with backtests results and some useful tools.