jesse-ai/jesse
Jesse
Jesse is a Python algorithmic trading framework used for developing, backtesting, and executing quantitative trading strategies. It functions as a trading strategy backtester and a machine learning trading platform, providing an environment to train predictive models on historical market data and deploy them into live strategies.
The framework features a standardized crypto exchange connectivity layer that allows for the execution of automated spot and futures trades across multiple cryptocurrency exchanges via an exchange-agnostic interface. It includes a quantitative risk analysis toolset to assess strategy robustness through Monte Carlo simulations and cross-validation testing.
The system covers a broad range of capabilities including algorithmic trade execution, time-series data backtesting to prevent look-ahead bias, and parameter optimization. It also provides tools for managing trade risk, monitoring real-time strategy performance, and programmatically researching strategies through scripts and notebooks.
Features
- Algorithmic Trading Frameworks - Provides a comprehensive modular platform for building, simulating, and executing automated quantitative trading strategies.
- Algorithmic Trading Platforms - Provides a comprehensive software suite for building and running automated cryptocurrency trading strategies.
- Time Series Backtesting - Iterates through historical price data chronologically to simulate performance while strictly preventing look-ahead bias.
- Event Driven Trading - Implements event-driven trading logic that executes based on specific market events or price candles.
- Algorithmic Trading Engines - Functions as a core engine for executing automated spot and futures trades based on algorithmic rules.
- Automated Trading Execution - Provides the infrastructure to automate the execution phase of trading strategies across multiple exchanges.
- Exchange Abstraction Layers - Provides a standardized abstraction layer to unify disparate cryptocurrency exchange APIs into a single common interface.
- Live Trading Execution - Automates the real-time execution of trades across multiple cryptocurrency exchanges based on custom rules.
- Trading Risk Analysis - Implements quantitative risk analysis through Monte Carlo simulations and cross-validation to assess strategy robustness.
- Trading Strategy Backtesters - Provides a high-fidelity backtesting engine to simulate strategy performance using historical time-series data while preventing look-ahead bias.
- Trading Strategy Frameworks - Provides a specialized software framework for converting trading ideas into executable algorithmic code.
- Event-Driven Engines - Features an event-driven engine that triggers strategy logic updates in response to new market candles.
- Exchange Connectivity APIs - Provides unified interfaces for connecting to multiple cryptocurrency exchange platforms via an agnostic layer.
- Strategy Integrations - Enables training models on historical data and deploying predictions to automate trading decisions.
- Predictive Trading Models - Offers an environment to train predictive machine learning models on historical data for automated trading decisions.
- Training and Testing Splits - Splits historical datasets into training and testing sets to verify strategy robustness and prevent overfitting.
- Automated Trading Research - Provides tools to run trading logic within scripts and notebooks for quantitative market research.
- Quantitative Trading Platforms - Provides a quantitative environment for researching market hypotheses via custom scripts and notebooks.
- Strategy Performance Analyzers - Ships tools for stress-testing results and shuffling trade orders to detect overfitting and verify strategy robustness.
- Market Indicator Visualization - Provides capabilities to visualize technical indicators and price action on charts for strategy analysis.
- Monte Carlo Sampling - Implements Monte Carlo simulations to generate randomized performance paths and assess the probability of future drawdowns.
- Risk Simulations - Generates randomized performance paths using historical trade data to estimate the probability of future drawdowns.
- Strategy Parameter Optimization - Implements batch simulations across hyperparameter ranges to identify the most performant quantitative trading settings.
- Trading Risk Management - Implements smart ordering logic for market, limit, and stop orders to protect capital and control position risk.
- Trading Strategy Optimizers - Runs parallel backtesting iterations to identify optimal strategy parameters and assess risk via simulations.
- Metric and Performance Monitors - Includes real-time logs and performance metrics to monitor the behavior of live trading strategies.
- Crypto Trading Bots - Advanced framework for researching and defining crypto strategies.
- Trading and Backtesting - Advanced crypto trading bot.
- Trading Bots - Advanced framework for researching and defining crypto strategies.
- Cryptocurrency Trading - Advanced framework for researching and defining crypto strategies.
Star history
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