Jesse - Algotrading cryptocurrencies in Python-crypto trading strategies in Python

Example

A traderJesse Algotrading Overview can define a class inheriting from Jesse's Strategy class and implement methods like should_long(), should_short(), go_long(), go_short(), update_position(), and on_open_position().

Scenario

A momentum trader implements a strategy where a long position is opened when RSI falls below 30 and closes when RSI exceeds 70. Jesse handles execution, position sizing, and logging automatically.

Example

Using historical candle data, a user can run backtests to simulate how their strategy would have performed in the past, accounting for fees, slippage, and execution constraints.

Scenario

A trader wants to test a moving average crossover strategy on BTC/USD over the past two years. Jesse runs the simulation and outputs performance metrics such as profit, drawdown, and win rate.

Example

Jesse provides built-in methods like self.stop_loss, self.take_profit, self.liquidate(), and utils.risk_to_qty() for managing trade size and risk exposure.

Scenario

A trader enters a long position on ETH and immediately sets a stop-loss at 2% below entry and a take-profit at 5% above entry using on_open_position(). Jesse automatically monitors the position and executes the orders.

Example

Jesse supports live trading on exchanges like Binance, Bybit, and FTX by linking API keys and handling order execution in real-time.

Scenario

A trader deploys a scalping strategy on BTC/USDT that executes trades every few seconds. Jesse ensures that each trade is executed according to the strategy's logic and monitors positions to prevent unwanted exposure.

Example

Jesse integrates with common technical indicators such as EMA, SMA, RSI, MACD, ATR, and ADX, which can be used directly in strategy code.

Scenario

A trader creates a trend-following strategy using the 21-period EMA and ATR-based stop-loss. Jesse provides the EMA and ATR calculations, which the trader can plug directly into their entry and exit logic.