QLNet
awesome-quant
| QLNet | awesome-quant | |
|---|---|---|
| 15 | 18 | |
| 372 | 15,977 | |
| - | - | |
| 6.0 | 8.8 | |
| 16 days ago | 7 days ago | |
| C# | Python | |
| BSD 3-clause "New" or "Revised" License | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
QLNet
awesome-quant
-
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.
-
Open Source Projects
This is a good list https://github.com/wilsonfreitas/awesome-quant
-
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
-
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
-
Hacker News top posts: Feb 22, 2022
A curated list of libraries, packages and resources for Quants\ (0 comments)
What are some alternatives?
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.
backtrader - Python Backtesting library for trading strategies
uniswap-sushiswap-arbitrage-bot - Two bots written in JS that uses flashswaps and normal swaps to arbitrage Uniswap. Includes an automated demostration.
CUDA.jl - CUDA programming in Julia.
awesome-discord-communities - A curated list of awesome Discord communities for programmers
Gekko-Strategies - Strategies to Gekko trading bot with backtests results and some useful tools.
WorldQuant_alpha101_code - Code implementation of the Quantigic 101 Formulaic Alphas
tdameritrade-matlab-api - TD Ameritrade API for MATLAB
akshare - AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库 [Moved to: https://github.com/akfamily/akshare]
bt - bt - flexible backtesting for Python
quant-trading - Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD