Jesse — AI Agent Framework: Live Stats & TrendScore

Jesse is a Python-based algorithmic trading framework focused on crypto. We track its GitHub activity and compare backtesting capabilities against FreqTrade and OctoBot.

GitHub data synced: Jun 25, 2026 • Sentiment updated: Jun 22, 2026

GitHub Statistics

Community Sentiment

Community Buzz: We stand on a lot of giant shoulders, Nearly all code involved in building new things is 'plagiarism', too from HackerNews

Pros & Cons

What People Love

Innovative tech from Dev.to, Gemini Embeddings from Dev.to

Common Complaints

Limited AI control, Bugs in GitHub

Biggest Positive: Innovative tech

Biggest Negative: Limited AI control

Why Jesse Stands Out

Jesse stands out from alternative trading frameworks with its simplicity, accuracy, and comprehensive feature set. Its simple syntax and extensive indicator library make it easy to define and backtest complex trading strategies, while its live trading capabilities and real-time monitoring features enable robust execution and risk management. Additionally, Jesse's built-in ML pipeline and integration with scikit-learn enable the development of predictive models for trading, setting it apart from other frameworks. By leveraging these features, traders can develop and execute effective trading strategies with ease.

Built With

Build a crypto trading bot that executes strategies across multiple timeframes — Jesse's simple syntax and comprehensive indicator library enable rapid development of complex trading strategies, Build a backtesting framework for algorithmic trading — Jesse's fast and accurate backtesting capabilities allow for thorough validation of trading strategies, Build a live trading platform with real-time monitoring and notifications — Jesse's live trading features and integrations with Telegram, Slack, and Discord enable robust monitoring and alerting, Build a risk management system with advanced alerting and position sizing — Jesse's built-in risk management tools and alerting system enable effective management of trading risks, Build a machine learning pipeline for trading strategy optimization — Jesse's built-in ML pipeline and integration with scikit-learn enable the development of predictive models for trading

Getting Started

  1. Install Jesse using pip: `pip install jesse`
  2. Configure your trading settings and credentials in the `config.json` file
  3. Define your trading strategy using Jesse's simple syntax, such as the example `GoldenCross` strategy provided in the README
  4. Backtest your strategy using the `backtest` command, specifying the desired timeframe and symbol
  5. Try live trading with the `live` command to verify that your strategy is executing correctly and receiving real-time updates

About

An advanced crypto trading bot written in Python

Official site: https://jesse.trade

Category & Tags

Category: trading

Tags: algo-trading, algorithmic-trading, bitcoin, bot, crypto, crypto-bot, crypto-bot-trading, cryptocurrency, framework, jesse, python, quantitative-finance, quantitative-trading, trade, trading, trading-algorithms, trading-bot, trading-strategies

Market Context

Competitive AI market