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Why sthewissen/Mynt is active?

The result is based on ratio of number of commits and code additions from initial and final time ranges.

Initial time range – from 10 Feb, 2023 to 13 May, 2023

Final time range – from 11 Nov, 2023 to 10 Feb, 2024

Additions and deletions stats are not available for this repository due to GitHub API limitations.

Data calculated on 10 Feb, 2024

Summary of sthewissen/Mynt

The sthewissen/Mynt GitHub repository is a platform for cryptocurrency trading bots and backtesting. The repository contains algorithms for different trading strategies and tools for backtesting these strategies against historical data.

The repository includes Mynt, which is a framework written in C# and designed for deriving high-quality trading models for cryptocurrencies. The project involves the use of features like backtesting and live trading with the goal of creating a trading bot that is advantageous for handling fluctuations in the cryptocurrency market.

The project handles candle data and lets the users define a set of rules that the trading bot should follow. It also provides support for using a unique strategy class for deriving profit from these defined rules. Through the use of this project, developers can make successful decisions about buying or selling digital assets.

Here is a basic overview of what the repository includes:

  • Interfaces for defining design patterns for the backtests and live trading using C#.
  • Core algorithm for creating a trading bot.
  • Example strategies for making profitable trades in the cryptocurrency market.
  • Algorithms for handling candle data from the CoinBase and Binance exchanges.
  • Tools for saving backtest results to an Azure Table Storage or CSV files.

The repository is regularly maintained, and it encourages contribution to improve the existing toolset.

Please note that while the repository provides powerful tools for cryptocurrency trading, the risk involved in real-world trading is high, and real trading should be handled with extreme caution, irrespective of the tools used.

Remember that past performance is not indicative of future results, and it is crucial to double-check everything before implementing it.

For the code structure, examples, and additional information, please check the repository located here.

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