Comparable Libraries and Packages to InferTrade

[hodlerfolyf]

Objectives:

• [ ] identifying similar packages and libraries utilised by developers, to gain a better understanding of where infertrade can add value and to avoid duplication of existing open source community efforts.
• [ ] determine which additional features or architectural changes would meet the needs of traders and developers.

Previous analysis :

Here are the top 15 open-source libraries (aside of numpy, pandas etc.). I ranked them based on the number of stars they have on github (they are also the most frequent in articles and on reddit).

zipline (13.5k stars) - https://github.com/quantopian/zipline freqtrade (6.8k stars) - https://github.com/freqtrade/freqtrade backtrader (5.9k stars) - https://github.com/mementum/backtrader ta-lib (4.8k stars) - https://github.com/mrjbq7/ta-lib YFinance (4.5k stars) - https://github.com/ranaroussi/yfinance PyAlgoTrade (3.2k stars) - https://github.com/gbeced/pyalgotrade TensorTrade (3k stars) - https://github.com/tensortrade-org/tensortrade Crypto-Signal (2.7k stars) - https://github.com/CryptoSignal/crypto-signal finmarketpy (2.5k stars) - https://github.com/cuemacro/finmarketpy catalyst (2k stars) - crypto-assets https://github.com/enigmampc/catalyst machine-learning-for-trading (2k stars) https://github.com/stefan-jansen/machine-learning-for-trading zvt (1.4k stars) - https://github.com/zvtvz/zvt/blob/master/README-en.md QTPyLib (1.2k stars) - https://github.com/ranaroussi/qtpylib backtesting.py (1.1k stars) - https://github.com/kernc/backtesting.py fooltrader (1k stars) - https://github.com/foolcage/fooltrader

Most of them are for backtesting, technical analysis, algotrading, but I also added those for data retrieval and those that analyse market.

Here are my notes with descriptions, and some more libraries Python open-source libraries for trading.docx

[ta-oliver]

Find Comparable Libraries and Packages for algorithmic trading.

@holderfolyf - I'd recommend spending ~5-10% of the time intended for a task on the description of what the task will be and the anticipated plan to carry it out. The remaining 90-95% task time generally ends up significantly more efficient (e.g. x 1.5 or x 2), which compensates for the overhead of describing the task clearly. So if you want to spend 4 hours researching then it is good to spend 10-20 minutes writing the description. If you are going to look for 30 minutes then 1-2 minutes is fine.

[hodlerfolyf]

Identifying Similar Packages and Libraries utilized by developers, this will help in getting a better understanding of requirements.

Determine what features are typically expected by users/developers.

Discover opportunities to build new features.

Optimize existing code.

Discovering New libraries to wrap and integrate with this repo to build better and more collaborative software.