Jesse - Algotrading cryptocurrencies in Python-algorithmic crypto trading framework.

Example

Users can define their trading strategies using Python by leveraging Jesse's strategy class. For example, a simple moving average crossover strategy can be implemented by defining the logic for entering and exiting trades based on the crossover of short and long moving averages.

Scenario

A trader wants to develop a strategy that buys Bitcoin when the 50-day moving average crosses above the 200-day moving average and sells when the opposite occurs. The trader can easily code this logic using Jesse's framework and then test it against historical data.

Example

Jesse allows users to backtest their strategies using historical market data to evaluate performance metrics like return, drawdown, and win/loss ratio. This is crucial for understanding how a strategy would have performed in the past.

Scenario

Before deploying a new trading strategy live, a user can backtest it on several years of historical data to identify its strengths and weaknesses. For instance, a strategy designed to trade Ethereum might be backtested to see how it would have performed during different market conditions, such as bull or bear markets.

Example

Jesse supports live trading by connecting to cryptocurrency exchanges via APIs. Once a strategy is thoroughly tested, it can be deployed to trade in real-time, executing orders based on the logic defined in the strategy.

Scenario

After successful backtesting, a trader decides to go live with their algorithm on a Binance account. The strategy continuously monitors the market and executes trades automatically according to the predefined rules, adjusting stop-losses and take-profits as necessary.