Freqtrade

Develop your trading strategy in Python using the Pandas library. Take inspiration from the example strategies available in the strategy repository. Download historical data for the exchange and markets you wish to trade with. Evaluate your strategy using the downloaded historical data. Utilize hyperoptimization, which utilizes machine learning techniques, to identify the optimal parameters for your strategy. You can optimize parameters such as buy, sell, take profit (ROI), stop-loss, and trailing stop-loss.

The objective is to identify the most favorable historical trade expectancy for different markets based on variations in the stop-loss parameter. Subsequently, determine which markets to include or exclude from your trading strategy. Determine the trade size based on a predefined risk percentage of your capital. You can conduct further analysis using either Backtesting data or Freqtrade trading history, which is stored in a SQL database. This analysis can include the generation of automated standard plots and methods to load the data into interactive environments.

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