How can we get a good trading repository?

<p id="isPasted">There are several open-source trading algorithms and frameworks with tested performance that you can explore. Here are some notable ones:</p><p><br></p><p>1. Backtrader</p><p>Description: A flexible Python framework for backtesting trading strategies. It supports multiple data feeds and allows for complex strategy development.</p><p>Performance: Users have reported success with various strategies, and the community provides numerous examples and documentation.</p><p>Repository: Backtrader GitHub</p><p><br></p><p>2. Zipline</p><p>Description: An open-source backtesting library for Python, originally developed by Quantopian. It allows users to create and test trading algorithms.</p><p>Performance: Zipline has been tested with various strategies and is known for its ease of use and integration with Pandas.</p><p>Repository: Zipline GitHub</p><p><br></p><p>3. QuantConnect / Lean</p><p>Description: QuantConnect provides a cloud-based algorithmic trading platform, and Lean is their open-source engine. It supports multiple asset classes and languages, including C# and Python.</p><p>Performance: QuantConnect has a large community and extensive documentation, with many algorithms shared and tested on the platform.</p><p>Repository: Lean GitHub</p><p><br></p><p>4. PyAlgoTrade</p><p>Description: A Python library that focuses on backtesting and trading strategy development. It’s designed to be simple and flexible.</p><p>Performance: It has been used in various trading competitions, and many users have shared successful strategies.</p><p>Repository: PyAlgoTrade GitHub</p><p><br></p><p>5. Quantlib</p><p>Description: While primarily a library for quantitative finance, it can be used to develop trading strategies, especially in options and derivatives.</p><p>Performance: QuantLib is widely used in the finance industry for pricing and risk management, which indirectly supports algorithm development.</p><p>Repository: QuantLib GitHub</p><p><br></p><p>6. Freqtrade</p><p>Description: A cryptocurrency trading bot written in Python that allows users to develop, backtest, and optimize trading strategies.</p><p>Performance: Freqtrade has a strong community and many pre-built strategies that have been tested in live markets.</p><p>Repository: Freqtrade GitHub</p><p><br></p><p>7. Keras-RL</p><p>Description: A library for reinforcement learning in Python using Keras. While not a trading algorithm per se, it can be used to develop trading strategies through reinforcement learning.</p><p>Performance: Users have applied various RL techniques to trading with varying degrees of success.</p><p>Repository: Keras-RL GitHub</p>