I built a trading algorithm that detects the most volatile coins on Binance and aims to buy before a potential mooning, other devs are helping making this into a web app.

This trading bot will automatically place trades on the Binance exchange by detecting the most volatile coins in a given timeframe.

The project gathered significant attention from the GitHub dev community, and have helped to make significant improvements to the original codebase, and I thought you guys might like it as well.

The code was built with customisability in mind, where each user can modify parameters like:

• Define what % increase constitues a buy signal
• Time elapsed between price checks
• Trailing stop loss / take profit
• Stop buying when the market is bearish

In addition to the core functionality, the algorithm is now able to receive and act on external signals which are fully customisable.

It uses data from Tradingview, and this returns buy signals according to the most relevant indicators. BY default if at least 18 out of 26 indicators agree that it's a good time to buy a certain coin, this will tell the bot to go buy that coin.

The bot also has a newly-implement pause functionality. It essentially analyses 15 moving average indicators and if half of them show that's a bearish market, the bot will not buy during this period.

You can also choose and pick any number of custom indicators from oscillators to moving averages and more. Those require a bit of coding, and I'm hoping to make it more user-friendly in the future but the core functionality is there.

If you're comfortable with Python and would like to inspect the source code please be my guest:

https://github.com/CyberPunkMetalHead/Binance-volatility-trading-bot

Otherwise, if you would like a more detailed guide, and want to try this out start here:

https://www.cryptomaton.org/2021/05/16/trailing-stop-loss-and-more-improvements-added-to-the-binance-volatility-trading-bot/

We're currently working on a roadmap to turn this into a working web-app, so there's no steep learning curve for anyone trying this out. You can follow the development progress on GitHub, or here as I will be posting updates on the progress.